• Microbiology and Biotechnology Letters
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• v.50 no.4
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• pp.533-547
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• 2022

This study investigated the hair growth effect of Schisandra chinensis extract (TS-SC) and TS-SC fermented by Lactobacillus plantarum (TS-SCLF) on human dermal papilla cells (hDPCs). The production of vascular endothelial growth factor (VEGF), insulin-like growth factor 1 (IGF-1), keratinocyte growth factor/fibroblast growth factor 7 (KGF/FGF-7) and hepatocyte growth factor (HGF), transforming growth factor beta 1 (TGF-β1) were examined. The secretion rates of VEGF and KGF/FGF-7 were high in TS-SC, and the secretion rates of IGF-1 and HGF were high in TS-SCLF. TGF-β1 was inhibited in a concentration-dependent manner in all samples. Gene expression of VEGF, IGF-1, KGF, HGF and alkaline phosphatase, relevant to hair growth, were examined. The data revealed that TS-SC and TS-SCLF successfully promoted hair growth in hDPCs. The IGF-1 gene was expressed in a dose-dependent manner in TS-SCLF. These results indicate that TS-SC and TS-SCLF fermented extract effectively promoted hair growth and gene expression relevant to hair growth in hDPCs. Used in clinical trials the test substance 'CMK-LPF01' showed a statistically significant increase in the number of hairs at 8 weeks, 16 weeks, and 24 weeks compared to before product use, and a change in hair growth, a secondary efficacy evaluation variable. Through additional research in the future, it is expected that "CMK-LPF01" can be developed as a functional material that can help alleviate symptoms of hair loss.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of the Korea Convergence Society
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• v.8 no.8
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• pp.267-281
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• 2017

Despite the recent importance of interdisciplinary convergence research, little has been studied on the detailed experience while conducting convergence research in Korea. To deeper understand challenges and overcoming in convergence research and to suggest an administrative system for it, by analyzing researchers experience and cognition while conducting their actual convergence research, in-depth interviews with 16 full-time faculty members with expertise in convergence research and research administration in a university and analyzed the interviews by using the paradigm model of grounded theory. The results show that the core phenomenon researchers experienced is that "convergence research is not being effectively conducted," and they experience growth in research through pursuing efforts and improving research atmosphere in various aspects. Based on these findings, we made several suggestions for promoting convergence research.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• The Plant Pathology Journal
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• v.33 no.5
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• pp.458-466
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• 2017

Xanthomonas oryzae pv. oryzae (Xoo), the causative agent of bacterial blight, is a major threat to rice productivity. Here, we performed RNA-Seq based transcriptomic analysis of Xoo transcripts isolated under in planta growth (on both susceptible and resistant hosts) and in vitro culture conditions. Our in planta extraction method resulted in successful enrichment of Xoo cells and provided RNA samples of high quality. A total of 4,619 differentially expressed genes were identified between in planta and in vitro growth conditions. The majority of the differentially expressed genes identified under in planta growth conditions were related to the nutrient transport, protease activity, stress tolerance, and pathogenicity. Among them, over 1,300 differentially expressed genes were determined to be secretory, including 184 putative type III effectors that may be involved in Xoo pathogenicity. Expression pattern of some of these identified genes were further validated by semi-quantitative RT-PCR. Taken together, these results provide a transcriptome overview of Xoo under in planta and in vitro growth conditions with a focus on its pathogenic processes, deepening our understanding of the behavior and pathogenicity of Xoo.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.5
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• pp.15-23
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• 2021

This study was carried out to suggest an appropriate plant combination by evaluating the growth of flowering ground-cover plants planted with Festuca glauca 'Eljiah Blue' on the roof-top environment. As for the plant materials, Allium senescens and Chrysanthemum coreanum which are shorter than Festuca glauca 'Eljiah Blue' and Sedum takesimense and Agastache rugosa which are taller than Festuca glauca 'Eljiah Blue' were selected. Festuca glauca 'Eljiah Blue' was planted on Conrol, and Festuca glauca 'Eljiah Blue' with Allium senescens (T1), Festuca glauca 'Eljiah Blue' with Sedum takesimense (T2), Festuca glauca 'Eljiah Blue' with Agastache rugosa(T3), and Festuca glauca 'Eljiah Blue' with Chrysanthemum coreanum (T4) were planted in each experimental plot. Plant height and covering rate were measured to evaluate the growth of Festuca glauca 'Eljiah Blue'. Also, relative growth rate (RGR) of plant height, RGR of plant width, and mortality rate of the flowering ground-cover plants were estimated. Plant height and cover rate of Festuca glauca 'Eljiah Blue' was greatest in T3. RGR of plant height was greater in the order of Agastache rugosa, Allium senescens, Chrysanthemum coreanum, and Sedum takesimense. In particular, RGR of plant width was also greatest for Agastache rugosa. Mortality rates of Agastache rugosa and Allium senescens were lowest at 11%. Therefore, based on good growth of Festuca glauca 'Eljiah Blue' planted with Agastache rugosa, these results were suggested as a desirable combination of plant species for rooftop gardening.

• The Journal of Economics, Marketing and Management
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• v.5 no.3
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• pp.1-11
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• 2017

E-infrastructural economy for ICT Living-Labs is a need for economic and cultural changes in various types of cars in accordance with the supply of the electric car. Depending on the number of cases by analyzing the supply and demand of electric vehicles among Korea and Northern Europe countries. it was indirectly proved that it makes economic growth. The research design is analyzed with the data and how to respond quickly to focus on the possibility of potential changes to the infrastructure realization and commercialization of government enterprises or electric cars through the ICT Living-Labs in Nordic countries. The data indicates that the leading commercialization emphasize on the development of the electric economic convergence and scalability for electric vehicle. When It shows the time of the infrastructure as ICT Living-Labs being delayed, it lowered growth target results for the development of the electric car industry in the future. All this is from the reason of opening the E-convergence economy over time. It is required that Korea should prepare E-convergence economy. Public regional energy should be present through the consistent selection of development for energy linking E-economy and E-trans distribution. Korea needs to be many difficulties in building the E- infrastructure for ICT Living-Labs. Unlike the Northern Europe it is to prepare the active support of both government and business. The role of the government discovers that the power generation through the quick selection of the industry, as well as to connect with the growth of the smart cities with the EVs industry.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.29 no.3
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• pp.140-142
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• 2019

AlN (Aluminum Nitride) crystal which could be used to substrates for UV LEDs was grown by PVT ((Physical Vapor Transport) method. 3 inch AlN single crystal with a thickenss of 4 mm was grown using Polycrystalline seed for 120 hours. In this report, a result of 3 inch polycrystalline bulk AlN growth behavior using large size crucible and growth condition were reported.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.303-312
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• 2019

The purpose of this study was to describe the semantic system and process of posttraumatic growth experience of firefighters after colleague's suicide. Grounded theory methodology based on symbolic interactionism was utilized. The subjects of this study were 7 firefighters who experienced the colleague's suicide. Data were collected by using in-depth interviews from October 1 to November 30, 2015. The results showed that "Gradually becoming more resilient" was analyzed to be the core category of firefighters' posttraumatic growth. We derived four categories and nine subcategories. Based on the results, it is necessary to develop a convergence intervention program to promote posttraumatic growth of firefighters exposed to various traumatic events.

• Journal of Environmental Science International
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• v.31 no.5
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• pp.405-411
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• 2022

Production of lettuce under indoor cultivation is highly affected by light intensity. In this study, we used shade cloth (commercial black net) to examine the effect of these condition on growth without an associated yield penalty of container-grown lettuce. Four levels of shading treatments (0%, 35%, 55%, and 75% referred to as Cont., S₃₅, S₅₅, and S₇₅ with respect to Cont.) and two lettuce varieties (red and blue) were evaluated. Variety-specific growth responses were observed with respect to different levels of shading treatments. High growth of red lettuce was occurred in Cont. treatment despite plant height and leaf length being higher than Cont. However, under 35% shading treatment blue lettuce was higher than in control plants. The highest root length was observed 0% shading (Cont.) of both varieties. These results reinforce the idea that blue lettuce is the better leafy vegetable rather than red lettuce for indoor cultivation related to lower light intensity environment conditions.