• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.45 no.2
• /
• pp.175-184
• /
• 2019

The epidermal growth factor (EGF) has a intrinsic function of inducing growth and proliferation of cells through interacting with cell membrane receptors in human epidermis and dermis layer. These functions of EGF are used as a main ingredient for wound healing medicines and anti-aging cosmetics. As a cosmetic ingredient, the EGF has a problem in exhibiting its natural efficacy due to the lack of the ability to penetrate through the stratum corneum, which is known as the skin barrier. In this study, a recombinant human epidermal growth factor ($MTD_{151}-EGF$) fused with the macromolecule transduction domain $(MTD)_{151}$ with the skin penetration ability was developed to improve the skin penetration efficiency of the EGF. Expression of $MTD_{151}-EGF$ was performed in E. coli transformed with a vector encoding the $MTD_{151}-EGF$ gene and then purified. The purified $MTD_{151}-EGF$ was evaluated using cell proliferation assay, cytotoxicity test and skin penetration test by franz diffusion cell assay and artificial skin. Cell proliferation activity of $MTD_{151}-EGF$ purified to high purity of 99% or above was equivalent to the EGF or better, and cytotoxicity was not observed. In addition, the $MTD_{151}-EGF$ showed an excellent penetration efficiency compared to the EGF in the skin penetration test with EGF and $MTD_{151}-EGF$ labeled by FITC in an artificial skin penetration model. Based on the quantitative analysis of the penetrating substance using franz diffusion cell assay, the amount of penetration was about 16 times more than that of EGF. These results can be regarded as an effective alternative to improve the existing physical transdermal penetration method related to the use of various active ingredients for cosmetics.

• Progress in Medical Physics
• /
• v.24 no.4
• /
• pp.237-242
• /
• 2013

In the intracranial regions, an accurate delineation of the target volume has been difficult with only the CT data due to poor soft tissue contrast of CT images. Therefore, the magnetic resonance images (MRI) for the delineation of the target volumes were widely used. To calculate dose distributions with MRI-based RTP, the electron density (ED) mapping concept from the diagnostic CT images and the pseudo CT concept from the MRI were introduced. In this study, the look up table (LUT) from the fifteen patients' diagnostic brain MRI images was created to verify the feasibility of MRI-based RTP. The dose distributions from the MRI-based calculations were compared to the original CT-based calculation. One MRI set has ED information from LUT (lMRI). Another set was generated with voxel values assigned with a homogeneous density of water (wMRI). A simple plan with a single anterior 6MV one portal was applied to the CT, lMRI, and wMRI. Depending on the patient's target geometry for the 3D conformal plan, 6MV photon beams and from two to five gantry portals were used. The differences of the dose distribution and DVH between the lMRI based and CT-based plan were smaller than the wMRI-based plan. The dose difference of wMRI vs. lMRI was measured as 91 cGy vs. 57 cGy at maximum dose, 74 cGt vs. 42 cGy at mean dose, and 94 cGy vs. 53 at minimum dose. The differences of maximum dose, minimum dose, and mean dose of the wMRI-based plan were lower than the lMRI-based plan, because the air cavity was not calculated in the wMRI-based plan. These results prove the feasibility of the lMRI-based planning for brain tumor radiation therapy.

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

• Journal of Cadastre & Land InformatiX
• /
• v.47 no.2
• /
• pp.107-120
• /
• 2017

There are many wildfire risk indices worldwide, but objective comparisons between such various wildfire risk indices and surface dryness indices have not been conducted for the wildfire cases in Korea. This paper describes a sensitivity analysis on the wildfire risk indices and surface dryness indices for Korea using LDAPS(Local Analysis and Prediction System) meteorological dataset on a 1.5-km grid and MODIS(Moderate-resolution Imaging Spectroradiometer) satellite images on a 1-km grid. We analyzed the meteorology-based wildfire risk indices such as the Australian FFDI(forest fire danger index), the Canadian FFMC(fine fuel moisture code), the American HI(Haines index), and the academically presented MNI(modified Nesterov index). Also we examined the satellite-based surface dryness indices such as NDDI(normalized difference drought index) and TVDI(temperature vegetation dryness index). As a result of the comparisons between the six indices regarding 120 wildfire cases with the area damaged over 1ha during the period between January 2013 and May 2017, we found that the FFDI and FFMC showed a good predictability for most wildfire cases but the MNI and TVDI were not suitable for Korea. The NDDI can be used as a proxy parameter for wildfire risk because its average CDF(cumulative distribution function) scores were stably high irrespective of fire size. The indices tested in this paper should be carefully chosen and used in an integrated way so that they can contribute to wildfire forecasting in Korea.

• Journal of Intelligence and Information Systems
• /
• v.19 no.3
• /
• pp.1-23
• /
• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.