• Korean Journal of Plant Resources
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• v.36 no.5
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• pp.455-468
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• 2023

We aimed to assess the potential growth-promoting effects of buckwheat sprout on intestinal bacteria and their anti-inflammation effects in a cellular model of intestinal inflammation. The growth of Bifidobacterium longum ssp. infantis BT1 was enhanced with the addition of the sprout extract of tartary buckwheat. Further, in the inflammatory model cells cultured with Raw 264.7 cells were treated with buckwheat sprout including each 10 probiotics before the addition of lipopolysaccharide (LPS) to induce inflammation in Raw 264.7 cells. Buckwheat sprout in both Bifidobacterium longum ssp. infantis BT1 and Lacticaseibacillus paracasei LPC5 significantly reduced the production of NO and PGE₂. The above results indicate that buckwheat sprout extract which contains with various physiologically active substances such as rutin, quercetin, and choline is effective in suppressing NO and PGE₂ production, which are inflammation-related indicators. The present study suggests that buckwheat sprout could induce positive effects on the intestinal beneficial bacteria and in anti-inflammation.

• The Journal of Korean Academy of Sensory Integration
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• v.21 no.2
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• pp.69-83
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• 2023

Objective : This study sought to systematically examine the intervention effect of social stories when applied in relation to children with sleep disorders. Methods : Studies available in the SCOPUS, ScienceDirect, PsycArticles, and PubMed databases that were published from 2001 to 2022 were searched. The keywords used for the search were as follows: ("social story" OR "social stories") AND ("sleep" OR "sleep disorders" OR "sleep wake disorder bedtimes" OR "sleep initiation and maintenance disorders" OR "sleep wake disorder" OR "sleep arousal disorders"). Based on the selection criteria, six experimental studies were selected and analyzed. Results : The selected studies were two randomized controlled trials, three individual trials, and one case study. The subjects were mostly children diagnosed with autism spectrum disorder who were school-aged or adolescent. The intervention types were often complex interventions, including social stories and other interventions, while the durations of the interventions varied from one day to more than 40 days. The interventions had a positive effect on the subjects' sleep quality, with night wakings, sleep onset delay, and sleep anxiety all being improved. As standardized assessment tools to evaluate the effectiveness of social stories, the Child Sleep Habits Questionnaire and the Child Behavior Checklist were used in two papers each, and were the most commonly used. As non-standardized assessment tools, each of the four papers used turbulence and sleep diaries as assessment tools. Conclusion : The effect of social story mediation can be divided into sleep quality and sleep-related behavior. In terms of sleep quality, studies showing improvements in night wakings, sleep onset delay, and sleep anxiety accounted for a large proportion of the sample. The detailed effect area of sleep quality showed a significant improvement after the interventions in most studies, and in all six studies analyzed in the present study, the continuation of the effect after the intervention was confirmed via follow-up tests. Thus, the findings of this study are expected to be helpful when applying social stories in children with sleep disorders in clinical practice due to presenting the intervention effects, outcome evaluation tools, and intervention periods in children with sleep disorders in prior investigations involving social stories.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.135-145
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• 2022

Due to COVID 19, we are facing unprecedented phenomena. Among various industries, the aviation industry, as it is directly affected by the corona crisis is actively establishing various survival strategies, such as expanding cargo transportation. The market size of air cargo transportation is continuously increasing due to the characteristics that distinguish it from other transportation, and as individualism and selfishness deepen in the aftermath of COVID 19, it can be inferred that the concept of reciprocity within distribution channels will become important in the post-corona era. The specific contents of this study(research question) are as follows. First, as a central member of the air logistics distribution channel, logistics service companies business process and the sub-dimensions of trust of are identified, and how trust is built with transportation companies is investigated. Second, the effect of such trust on the various performances of logistics service companies is analyzed. Third, we examine whether the influences of the sub-dimensions of trust change according to the perceived reciprocity of logistics service companies. In addition, we investigate whether the perceived reciprocity changed before and after the corona situation. In particular, this study theoretically integrates the concepts of trust and commitment, which have been distinguished in many prior studies, to improve the parsimony and practicality of the research model. This study will be able to present useful academic and practical implications by empirically examining how trust is built between members of the air logistics distribution channel and furthermore, how much it affects the performance of logistics service companies, and by identifying the moderating effect of reciprocity.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.67-76
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• 2024

In this paper, we present a DIP-MLS testing method that combines digital image processing with a rigid body-based MLS differencing approach to measure mechanical variables and analyze the impact of target location and image resolution. This method assesses the displacement of the target attached to the sample through digital image processing and allocates this displacement to the node displacement of the MLS differencing method, which solely employs nodes to calculate mechanical variables such as stress and strain of the studied object. We propose an effective method to measure the displacement of the target's center of gravity using digital image processing. The calculation of mechanical variables through the MLS differencing method, incorporating image-based target displacement, facilitates easy computation of mechanical variables at arbitrary positions without constraints from meshes or grids. This is achieved by acquiring the accurate displacement history of the test specimen and utilizing the displacement of tracking points with low rigidity. The developed testing method was validated by comparing the measurement results of the sensor with those of the DIP-MLS testing method in a three-point bending test of a rubber beam. Additionally, numerical analysis results simulated only by the MLS differencing method were compared, confirming that the developed method accurately reproduces the actual test and shows good agreement with numerical analysis results before significant deformation. Furthermore, we analyzed the effects of boundary points by applying 46 tracking points, including corner points, to the DIP-MLS testing method. This was compared with using only the internal points of the target, determining the optimal image resolution for this testing method. Through this, we demonstrated that the developed method efficiently addresses the limitations of direct experiments or existing mesh-based simulations. It also suggests that digitalization of the experimental-simulation process is achievable to a considerable extent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• The Korean Society of Law and Medicine
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• v.24 no.4
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• pp.67-101
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• 2023

The utilization of generative AI in the medical field is also being rapidly researched. Access to vast data sets reduces the time and energy spent in selecting information. However, as the effort put into content creation decreases, there is a greater likelihood of associated issues arising. For example, with generative AI, users must discern the accuracy of results themselves, as these AIs learn from data within a set period and generate outcomes. While the answers may appear plausible, their sources are often unclear, making it challenging to determine their veracity. Additionally, the possibility of presenting results from a biased or distorted perspective cannot be discounted at present on ethical grounds. Despite these concerns, the field of generative AI is continually advancing, with an increasing number of users leveraging it in various sectors, including biomedical and life sciences. This raises important legal considerations regarding who bears responsibility and to what extent for any damages caused by these high-performance AI algorithms. A general overview of issues with generative AI includes those discussed above, but another perspective arises from its fundamental nature as a large-scale language model ('LLM') AI. There is a civil law concern regarding "the memorization of training data within artificial neural networks and its subsequent reproduction". Medical data, by nature, often reflects personal characteristics of patients, potentially leading to issues such as the regeneration of personal information. The extensive application of generative AI in scenarios beyond traditional AI brings forth the possibility of legal challenges that cannot be ignored. Upon examining the technical characteristics of generative AI and focusing on legal issues, especially concerning the protection of personal information, it's evident that current laws regarding personal information protection, particularly in the context of health and medical data utilization, are inadequate. These laws provide processes for anonymizing and de-identification, specific personal information but fall short when generative AI is applied as software in medical devices. To address the functionalities of generative AI in clinical software, a reevaluation and adjustment of existing laws for the protection of personal information are imperative.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1501-1512
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• 2024

Plasma disruption in tokamak experiments is a challenging issue that causes damage to the device. Reliable prediction methods are needed, but the lack of full understanding of plasma disruption limits the effectiveness of physics-driven methods. Data-driven methods based on supervised learning are commonly used, and they rely on labelled training data. However, manual labelling of disruption precursors is a time-consuming and challenging task, as some precursors are difficult to accurately identify. The mainstream labelling methods assume that the precursor onset occurs at a fixed time before disruption, which leads to mislabeled samples and suboptimal prediction performance. In this paper, we present disruption prediction methods based on anomaly detection to address these issues, demonstrating good prediction performance on J-TEXT and EAST. By evaluating precursor onset times using different anomaly detection algorithms, it is found that labelling methods can be improved since the onset times of different shots are not necessarily the same. The study optimizes precursor labelling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.

• Information Systems Review
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• v.19 no.2
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• pp.1-19
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• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.4
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• pp.422-430
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• 2023

The nutrient-rich and climate-resilient finger millet (Eleusine coracana (L.) Gaertn.) is a relatively new crop on the agricultural landscape. The present study explores the agronomic characteristics and antioxidant activities of grains and cookies produced from 'Finger1ho,' which was the first finger millet variety developed in South Korea. With heightened calcium content (314 mg/100 g) and polyphenol levels, 'Finger1ho' exhibited superior radical scavenging activities compared to other millets. The investigation assessed the impact of whole finger millet flour at varying concentrations (0, 10, 30, 50, and 100%) on cookie properties. Increasing the substitution of finger millet flour in the cookie formulation resulted in a notable rise in calcium content, ranging from 1.8 times at 10% to an impressive 10.8 times at 100%, surpassing the levels found in conventional wheat cookies. Conversely, the sodium (Na) levels in finger millet cookies demonstrated minimal variations, presenting a potentially favorable aspect in addressing the high Na intake prevalent in the South Korean diet. Notably, the antioxidant activity, assessed through ABTS and DPPH radical scavenging assays, exhibited a significant elevation compared to the control. This increase in antioxidant activity was directly proportional to the quantity of finger millet incorporated (p<0.001), indicating the potential health benefits associated with higher levels of finger millet in the cookie formulation. This study highlights finger millet's potential as a beneficial ingredient, enhancing both consumer acceptability and the functional attributes of cookies. Notably, cookies with 10% to 50% added finger millet exhibited significantly superior quality characteristics compared to controls, suggesting promising avenues for health-functional cookie development.

• Korean Journal of Heritage: History & Science
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• v.57 no.1
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• pp.82-108
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• 2024

With the transition of archaeological recording method's transition from analog to digital, the 3D scanning technology has been actively adopted within the field. Research on the digital archaeological digital data gathered from 3D scanning and photogrammetry is continuously being conducted. However, due to cost and manpower issues, most buried cultural heritage organizations are hesitating to adopt such digital technology. This paper aims to present a digital recording method of relics utilizing open-source software and photogrammetry technology, which is believed to be the most efficient method among 3D scanning methods. The digital recording process of relics consists of three stages: acquiring a 3D model, creating a joining map with the edited 3D model, and creating an digital drawing. In order to enhance the accessibility, this method only utilizes open-source software throughout the entire process. The results of this study confirms that in terms of quantitative evaluation, the deviation of numerical measurement between the actual artifact and the 3D model was minimal. In addition, the results of quantitative quality analysis from the open-source software and the commercial software showed high similarity. However, the data processing time was overwhelmingly fast for commercial software, which is believed to be a result of high computational speed from the improved algorithm. In qualitative evaluation, some differences in mesh and texture quality occurred. In the 3D model generated by opensource software, following problems occurred: noise on the mesh surface, harsh surface of the mesh, and difficulty in confirming the production marks of relics and the expression of patterns. However, some of the open source software did generate the quality comparable to that of commercial software in quantitative and qualitative evaluations. Open-source software for editing 3D models was able to not only post-process, match, and merge the 3D model, but also scale adjustment, join surface production, and render image necessary for the actual measurement of relics. The final completed drawing was tracked by the CAD program, which is also an open-source software. In archaeological research, photogrammetry is very applicable to various processes, including excavation, writing reports, and research on numerical data from 3D models. With the breakthrough development of computer vision, the types of open-source software have been diversified and the performance has significantly improved. With the high accessibility to such digital technology, the acquisition of 3D model data in archaeology will be used as basic data for preservation and active research of cultural heritage.