• Radiation Oncology Journal
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• v.24 no.1
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• pp.67-76
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• 2006

Purpose: Oral mucositis is a common toxicity of radiation or chemotherapy, which is used a treatment for head and neck cancer. We investigated effects of recombinant human epidermal growth factor (rhEGF) on radiation-induced oral mucositis in rat model. Materials and Methods: Spraque-Dawley rats (7 per group) exposed to a single dose of 25 Gy (day 0) on their head, except for one group, were randomly divided into un-treated, vehicle-treated, and two rhEGF-treated groups. Rats were topically applied with rhEGF (15 or $30{\mu}g/oral$ cavity/day) or vehicle to their oral mucosa. Survival rate of rats, weight changes, and food intakes were examined from day 0 to 18 after radiation. Histology study was performed from oral mucosa of rats at day 7 and 18 after radiation. Results: rhEGF-treated groups (15 or $30{\mu}g/oral$) showed all survival rate 33%, whereas un-treated and vehicle-treated groups showed all survival rate 0% at the end of experiment. rhEGF-treated groups statistically had less weight loss compared to vehicle-treated group from day 2 to 7 after radiation. Food intake of rats with rhEGF treatment turned to increase at day 14 after radiation. At 7 day after radiation, un-treated and vehicle-treated groups showed severe pseudomembraneous or ulcerative oral mucositis. On the other hand, rhEGF-treated groups had no more than cellular swelling and degeneration of epidermal cells in oral mucosa of rats. Conclusion: These results suggest that rhEGF has significantly positive effects on radiation-induced oral mucositis in rats. rhEGF display a therapeutic potential on a clinical level.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.127-142
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• 2016

Deep learning model is a kind of neural networks that allows multiple hidden layers. There are various deep learning architectures such as convolutional neural networks, deep belief networks and recurrent neural networks. Those have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. Among those architectures, convolutional neural networks and recurrent neural networks are classified as the supervised learning model. And in recent years, those supervised learning models have gained more popularity than unsupervised learning models such as deep belief networks, because supervised learning models have shown fashionable applications in such fields mentioned above. Deep learning models can be trained with backpropagation algorithm. Backpropagation is an abbreviation for "backward propagation of errors" and a common method of training artificial neural networks used in conjunction with an optimization method such as gradient descent. The method calculates the gradient of an error function with respect to all the weights in the network. The gradient is fed to the optimization method which in turn uses it to update the weights, in an attempt to minimize the error function. Convolutional neural networks use a special architecture which is particularly well-adapted to classify images. Using this architecture makes convolutional networks fast to train. This, in turn, helps us train deep, muti-layer networks, which are very good at classifying images. These days, deep convolutional networks are used in most neural networks for image recognition. Convolutional neural networks use three basic ideas: local receptive fields, shared weights, and pooling. By local receptive fields, we mean that each neuron in the first(or any) hidden layer will be connected to a small region of the input(or previous layer's) neurons. Shared weights mean that we're going to use the same weights and bias for each of the local receptive field. This means that all the neurons in the hidden layer detect exactly the same feature, just at different locations in the input image. In addition to the convolutional layers just described, convolutional neural networks also contain pooling layers. Pooling layers are usually used immediately after convolutional layers. What the pooling layers do is to simplify the information in the output from the convolutional layer. Recent convolutional network architectures have 10 to 20 hidden layers and billions of connections between units. Training deep learning networks has taken weeks several years ago, but thanks to progress in GPU and algorithm enhancement, training time has reduced to several hours. Neural networks with time-varying behavior are known as recurrent neural networks or RNNs. A recurrent neural network is a class of artificial neural network where connections between units form a directed cycle. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal memory to process arbitrary sequences of inputs. Early RNN models turned out to be very difficult to train, harder even than deep feedforward networks. The reason is the unstable gradient problem such as vanishing gradient and exploding gradient. The gradient can get smaller and smaller as it is propagated back through layers. This makes learning in early layers extremely slow. The problem actually gets worse in RNNs, since gradients aren't just propagated backward through layers, they're propagated backward through time. If the network runs for a long time, that can make the gradient extremely unstable and hard to learn from. It has been possible to incorporate an idea known as long short-term memory units (LSTMs) into RNNs. LSTMs make it much easier to get good results when training RNNs, and many recent papers make use of LSTMs or related ideas.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.227-232
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• 2018

Purpose: Febrile seizure (FS) is the most common type of seizure in children between 6 months to 5 years of age. A family history of febrile seizures can increase the risk a child will have a FS. Yet, prevalence of FS regarding external environment has not been clearly proved. This study attempts to determine the association between prevalence of FS and weather. Methods: This study included medical records from the Korea National Health Insurance Review and Assessment Service. Data were collected from 29,240 children, born after 2004, diagnosed with FS who were admitted to one of the hospitals in Seoul, Korea, between January 2009 and December 2013. During the corresponding time period, data from the Korea Meteorological Administration on daily monitoring of four meteorological factors (sea-level pressure, amount of precipitation, humidity and temperature) were collected. The relationships of FS prevalence and each meteorological factor will be designed using Poisson generalized additive model (GAM). Also, the contributory effect of viral infections on FS prevalence and weather will be discussed. Results: The amount of precipitation was divided into two groups for comparison: one with less than 5 mm and the other with equal to or more than 5 mm. As a result of Poisson GAM, higher prevalence of FS showed a correlation with smaller amount of precipitation. Smoothing function was used to classify the relationships between three variables (sea-level pressure, humidity, and temperature) and prevalence of FS. FS prevalence was correlated with lower sea-level pressure and lower humidity. FS prevalence was high in two temperature ranges (-7 to $-1^{\circ}C$ and $18-21^{\circ}C$). Conclusion: Low sea-level pressure, small amount of precipitation, and low relative air humidity may increase FS prevalence risk.

• International Area Studies Review
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• v.22 no.1
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• pp.215-235
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• 2018

Policy concerns related to raising fertility rates are not only common interests among the OECD countries, but they are also issues of great concern to South Korea whose fertility rate is the lowest in the world. The fertility rate in South Korea continues to decline, even though most of the national budget has been spent on measures to address this and many studies have been conducted on the increase in the fertility rates. In this regard, this study aims to verify the effectiveness of the detailed factors affecting the fertility rate that have been discussed in the previous studies on fertility rates, and to investigate the overall trend toward enhancing the quality of life and increasing the fertility rate through macroscopic and structural studies under the recognition of problems related to the policy approaches through the case studies of the European countries. Toward this end, this study investigated if a high quality of life in advanced countries contributes to the increase in the fertility rate, which country serves as a state model that has a high quality of life and a high fertility rate, and what kind of social and policy environment does the country have with regard to childbirth. The analysis of the OECD Better Life Index (BLI) and CIA fertility rate data showed that the countries whose people enjoy a high quality of life do not necessarily have high fertility rates. In addition, under the recognition that a country with a high quality of life and a high birth rate serves as a state model that South Korea should aim for, the social characteristics of Iceland, Ireland, and New Zealand, which turned out to have both a high quality of life and a high fertility rate, were compared with those of Germany, which showed a high quality of life but a low fertility rate. According to the comparison results, the three countries that were mentioned showed higher awareness of gender equality; therefore, the gender wage gap was small. It was also confirmed that the governments of these countries support various policies that promote both parents sharing the care of their children. In Germany, on the other hand, the gender wage gap was large and the fertility rate was low. In a related move, however, the German government has made active efforts to a paradigm shift toward gender equality. The fertility rate increases when the synergy lies in the relationship between parents and children; therefore, awareness about gender equality should be firmly established both at home and in the labor market. For this reason, the government is required to provide support for the childbirth and rearing environment through appropriate family policies, and exert greater efforts to enhance the effectiveness of the relevant systems rather than simply promoting a system construction. Furthermore, it is necessary to help people in making their own childbearing decisions during the process of creating a better society by changing the national goal from 'raising the fertility rate' to 'creating a healthy society made of happy families'

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.13-29
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• 2021

The purpose of this study was to analyze the spatial distribution and location factors of startups by generation (young and middle-aged) in Seoul. To this end, a research model was established that included factors of industry, population, and startup institutions by generation in 424 administrative districts using the Seoul Business Enterprise Survey(2018), which includes data on the age group of entrepreneurs. As an analysis method, descriptive statistics were conducted to confirm the frequency, average and standard deviation of startups by generation and major variables in the administrative districts of Seoul, and spatial distribution and characteristics of startups by generation were analyzed through global and local spatial autocorrelation analysis. In particular, the spatial distribution of startups in Seoul was confirmed in-depth by categorizing and analyzing startups by major industries. Afterwards, an appropriate spatial regression analysis model was selected through the Lagrange test, and based on this, the location factors affecting startups by generation were analyzed. The main results derived from the research results are as follows. First, there was a significant difference in the spatial distribution of young and middle-aged startups. The young people started to startups in the belt-shaped area that connects Seocho·Gangnam-Yongsan-Mapo-Gangseo, while middle-aged people were relatively active in the southeastern region represented by Seocho, Gangnam, Songpa, and Gangdong. Second, startups by generation in Seoul showed various spatial distributions according to the type of business. In the knowledge high-tech industries(ICT, professional services) in common, Seocho, Gangnam, Mapo, Guro, and Geumcheon were the centers, and the manufacturing industry was focused on existing clusters. On the other hand, in the case of the life service industry, young people were active in startups near universities and cultural centers, while middle-aged people were concentrated on new towns. Third, there was a difference in factors that influenced the startup location of each generation in Seoul. For young people, high-tech industries, universities, cultural capital, and densely populated areas were significant factors for startup, and for middle-aged people, professional service areas, low average age, and the level of concentration of start-up support institutions had a significant influence on startup. Also, these location factors had different influences for each industry. The implications suggested through the study are as follows. First, it is necessary to support systematic startups considering the characteristics of each region, industry, and generation in Seoul. As there are significant differences in startup regions and industries by generation, it is necessary to strengthen a customized startup support system that takes into account these regional and industrial characteristics. Second, in terms of research methods, a follow-up study is needed that comprehensively considers culture and finance at the large districts(Gu) level through data accumulation.

• Korean Journal of Heritage: History & Science
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• v.55 no.1
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• pp.175-198
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• 2022

With the development of technology, the methods of digitally converting various forms of analog information have become common. As a result, the concept of recording, building, and reproducing data in a virtual space, such as digital heritage and digital reconstruction, has been actively used in the preservation and research of various cultural heritages. However, there are few existing research results that suggest optimal scanners for small and medium-sized relics. In addition, scanner prices are not cheap for researchers to use, so there are not many related studies. The 3D scanner specifications have a great influence on the quality of the 3D model. In particular, since the state of light reflected on the surface of the object varies depending on the type of light source used in the scanner, using a scanner suitable for the characteristics of the object is the way to increase the efficiency of the work. Therefore, this paper conducted a study on nine small and medium-sized buried cultural properties of various materials, including earthenware and porcelain, by period, to examine the differences in quality of the four types of 3D scanners. As a result of the study, optical scanners and small and medium-sized object scanners were the most suitable digital records of the small and medium-sized relics. Optical scanners are excellent in both mesh and texture but have the disadvantage of being very expensive and not portable. The handheld method had the advantage of excellent portability and speed. When considering the results compared to the price, the small and medium-sized object scanner was the best. It was the photo room measurement that was able to obtain the 3D model at the lowest cost. 3D scanning technology can be largely used to produce digital drawings of relics, restore and duplicate cultural properties, and build databases. This study is meaningful in that it contributed to the use of scanners most suitable for buried cultural properties by material and period for the active use of 3D scanning technology in cultural heritage.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Korean Journal of Environmental Biology
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• v.41 no.2
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• pp.109-125
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• 2023

This study was conducted to select target fish species as baseline research for accumulation analysis of major hazardous chemicals entering the aquatic ecosystem in Korea and to analyze the impact on fish community. The test bed was selected from a sewage treatment plant, which could directly confirm the impact of the inflow of harmful chemicals, and the Geum River estuary where harmful chemicals introduced into the water system were concentrated. A multivariable metric model was developed to select target candidate fish species for hazardous chemical analysis. Details consisted of seven metrics: (1) commercially useful metric, (2) top-carnivorous species metric, (3) pollution fish indicator metric, (4) tolerance fish metric, (5) common abundant metric, (6) sampling availability (collectability) metric, and (7) widely distributed fish metric. Based on seven metric models for candidate fish species, eight species were selected as target candidates. The co-occurring dominant fish with target candidates was tolerant (50%), indicating that the highest abundance of tolerant species could be used as a water pollution indicator. A multi-metric fish-based model analysis for aquatic ecosystem health evaluation showed that the ecosystem health was diagnosed as "bad conditions". Physicochemical water quality variables also influenced fish feeding and tolerance guild in the testbed. Eight water quality parameters appeared high at the T1 site, indicating a large impact of discharging water from the sewage treatment plant. T2 site showed massive algal bloom, with chlorophyll concentration about 15 times higher compared to the reference site.

• Journal of the Korean Geotechnical Society
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• v.24 no.4
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• pp.101-114
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• 2008

Pressurized grouting is a common technique in geotechnical engineering applications to increase the stiffness and strength of the ground mass and to fill boreholes or void space in a tunnel lining and so on. Recently, the pressurized grouting has been applied to a soil-nailing system which is widely used to improve slope stability. Because interaction between pressurized grouting paste and adjacent ground mass is complicated and difficult to analyze, the soil-nailing design has been empirically performed in most geotechnical applications. The purpose of this study is to analyze the ground behavior induced by pressurized grouting paste with the aid of laboratory model tests. The laboratory tests are carried out for four kinds of granitic residual soils. When injecting pressure is applied to grout, the pressure measured in the adjacent ground initially increases for a while, which behaves in the way of the membrane model. With the lapse of time, the pressure in the adjacent ground decreases down to a value of residual stress because a portion of water in the grouting paste seeps into the adjacent ground. The seepage can be indicated by the fact that the ratio of water/cement in the grouting paste has decreased from a initial value of 50% to around 30% during the test. The reduction of the W/C ratio should cause to harden the grouting paste and increase the stiffness of it, which restricts the rebound of out-moved ground into the original position, and thus increase the in-situ stress by approximately 20% of the injecting pressures. The measured radial deformation of the ground under pressure is in good agreement with the expansion of a cylindrical cavity estimated by the cavity expansion theory. In-situ test revealed that the pullout resistance of a soil nailing with pressurized grouting is about 36% larger than that with regular grouting, caused by grout radius increase, residual stress effect, and/or roughness increase.

• Korea Science and Art Forum
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• v.25
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• pp.103-118
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• 2016

Due to the rapid development of ICT, corporate business models quickly changed and because of the radical growth of IT technology, sequential or gradual survival has become difficult. Internet-based new businesses such as IT service companies are seeking for new convergence business models that have not existed before to create business models that are more competitive, but the economic efficiency of business models that were successful in the past is wearing off. Yet, as reaching the critical point where the platform value becomes extremely high for platforms via the Internet is happening at a much higher speed than before, platform-ization has become a very important condition for rapid business expansion for all kinds of businesses. This study analyzes the necessity of establishing platform business models in IT service planning and identifies their characteristics through case analyses of platform business models. The results derived features First, there is a need to ensure sufficient buyers and sellers, and second, platform business model should provide customers with distinctive value of the only platforms are generating. third, the common interests between platform-driven company and a partner, participants Should be existing. Fourthly, by expanding base of participants and upgrades, expansion of adjacent areas we must have a continuous scalability and evolution must be sustainable. While it is expected that the identified characteristics will cause tremendous impacts to the establishment of platform business models and to the graphing of service planning, we also look forward to this study serving as the starting point for the establishment of theories of profit models for platform businesses, which were not mentioned in the study, so that planners responsible for platform-based IT service planning will spend less time and draw bigger schemes in building planning drafts.