• Journal of Life Science
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• v.29 no.3
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• pp.295-302
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• 2019

Liquiritigenin (LG) is a chiral flavonoid isolated from the roots of licorice. It exhibits multiple biological activities including anti-oxidant, anti-cancer, and anti-inflammatory effects. In particular though, the anti-cancer activity of LG in oral squamous cell carcinoma has yet to be elucidated, and LG-induced apoptosis in oral squamous cell carcinoma remains poorly understood. In the present study, we tested the role of LG in inducing apoptosis in oral squamous cell carcinoma cells. LG treatment of HN22 cells resulted in a dose-dependent inhibition of cell viability as detected by a 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide assay. The induction of apoptosis in terms of Annexin V/7-Aminoactinomycin D staining, sub-G1 population, and multi-caspase activity were assessed with a $Muse^{TM}$ Cell Analyzer. Flow cytometric analysis revealed that LG treatment resulted in G2/M arrest in cell cycle progression and downregulation of cyclin B1 and CDC2 expression in a concentration-dependent manner. It also resulted in significant upregulation of p27. In addition, LG was seen to trigger the generation of reactive oxygen species and induce CCAAT/enhancer-binding protein homologous protein and 78-kDa glucose-regulated protein in concentration-dependent upregulation. The LG treatment of HN22 cells led to a loss of mitochondrial membrane potential (${\Delta}{\Psi}m$); it also reduced the levels of anti-apoptotic protein and increased the expression of apoptotic protease activating factor-1, cleaved poly (ADP-ribose)polymerase and Bax. Overall, our results indicate that the pro-apoptotic effects of LG in HN22 cells depend on the activation of both intrinsic and extrinsic signaling pathways. Thus, our results suggest that LG constitutes a natural compound with a potential role as an anti-tumor agent in oral squamous cell carcinoma.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.227-239
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• 2019

Heavy snowfall events frequently occur in the Gangwon province, and the snowfall amount significantly varies in space due to the complex terrain and topographical modulation of precipitation. Understanding the spatial characteristics of heavy snowfall and its prediction is particularly challenging during snowfall events in the easterly winds. The easterly wind produces a significantly different atmospheric condition. Hence, it brings different precipitation characteristics. In this study, we have investigated the microphysical characteristics of snowfall in the windward and leeward sides of the Taebaek mountain range in the easterly condition. The two snowfall events are selected in the easterly, and the snow particles size distributions (SSD) are observed in the four sites (two windward and two leeward sites) by the PARSIVEL distrometers. We compared the characteristic parameters of SSDs that come from leeward sites to that of windward sites. The results show that SSDs of windward sites have a relatively wide distribution with many small snow particles compared to those of leeward sites. This characteristic is clearly shown by the larger characteristic number concentration and characteristic diameter in the windward sites. Snowfall rate and ice water content of windward also are larger than those of leeward sites. The results indicate that a new generation of snowfall particles is dominant in the windward sites which is likely due to the orographic lifting. In addition, the windward sites show heavy aggregation particles by nearby zero ground temperature that is likely driven by the wet and warm condition near the ocean.

• Journal of the Korean Electrochemical Society
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• v.22 no.3
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• pp.128-137
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• 2019

A lithium is the lightest metal on the earth. It has some attractive characteristics as a negative electrode material such as a low reduction potential (-3.04 V vs. SHE) and a high theoretical capacity ($3,860mAh\;g^{-1}$). Therefore, it has been studied as a next generation anode material for high energy lithium batteries. The thin lithium electrode is required to maximize the efficiency and energy density of the battery, but the physical roll-press method has a limitation in manufacturing thin lithium. In this study, thin lithium electrode was fabricated by electrodeposition under various conditions such as compositions of electrolytes and the current density. Deposited lithium showed strong relationship between process condition and its characteristics. The concentration of electrolyte affects to the shape of deposited lithium particle. As the concentration increases, the shape of particle changes from a sharp edged long one to a rounded lump. The former shape is favorable for suppressing dendrite formation and the elec-trode shows good stripping efficiency of 92.68% (3M LiFSI in DME, $0.4mA\;cm^{-2}$). The shape of deposited particle also affected by the applied current density. When the amount of current applied gets larger the shape changes to the sharp edged long one like the case of the low concentration electrolyte. The combination of salts and solvents, 1.5M LiFSI + 1.5M LiTFSI in DME : DOL [1 : 1 vol%] (Du-Co), was applied to the electrolyte for the lithium deposition. The lithium electrode obtained from this electrolyte composition shows the best stripping efficiency (97.26%) and the stable reversibility. This is presumed to be due to the stability of the surface film induced by the Li-F component and the DOL effect of providing film flexibility.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.19-41
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• 2019

According to the rapidly increasing demand for text data analysis, research and investment in text mining are being actively conducted not only in academia but also in various industries. Text mining is generally conducted in two steps. In the first step, the text of the collected document is tokenized and structured to convert the original document into a computer-readable form. In the second step, tasks such as document classification, clustering, and topic modeling are conducted according to the purpose of analysis. Until recently, text mining-related studies have been focused on the application of the second steps, such as document classification, clustering, and topic modeling. However, with the discovery that the text structuring process substantially influences the quality of the analysis results, various embedding methods have actively been studied to improve the quality of analysis results by preserving the meaning of words and documents in the process of representing text data as vectors. Unlike structured data, which can be directly applied to a variety of operations and traditional analysis techniques, Unstructured text should be preceded by a structuring task that transforms the original document into a form that the computer can understand before analysis. It is called "Embedding" that arbitrary objects are mapped to a specific dimension space while maintaining algebraic properties for structuring the text data. Recently, attempts have been made to embed not only words but also sentences, paragraphs, and entire documents in various aspects. Particularly, with the demand for analysis of document embedding increases rapidly, many algorithms have been developed to support it. Among them, doc2Vec which extends word2Vec and embeds each document into one vector is most widely used. However, the traditional document embedding method represented by doc2Vec generates a vector for each document using the whole corpus included in the document. This causes a limit that the document vector is affected by not only core words but also miscellaneous words. Additionally, the traditional document embedding schemes usually map each document into a single corresponding vector. Therefore, it is difficult to represent a complex document with multiple subjects into a single vector accurately using the traditional approach. In this paper, we propose a new multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. This study targets documents that explicitly separate body content and keywords. In the case of a document without keywords, this method can be applied after extract keywords through various analysis methods. However, since this is not the core subject of the proposed method, we introduce the process of applying the proposed method to documents that predefine keywords in the text. The proposed method consists of (1) Parsing, (2) Word Embedding, (3) Keyword Vector Extraction, (4) Keyword Clustering, and (5) Multiple-Vector Generation. The specific process is as follows. all text in a document is tokenized and each token is represented as a vector having N-dimensional real value through word embedding. After that, to overcome the limitations of the traditional document embedding method that is affected by not only the core word but also the miscellaneous words, vectors corresponding to the keywords of each document are extracted and make up sets of keyword vector for each document. Next, clustering is conducted on a set of keywords for each document to identify multiple subjects included in the document. Finally, a Multi-vector is generated from vectors of keywords constituting each cluster. The experiments for 3.147 academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the proposed multi-vector based method, we ascertained that complex documents can be vectorized more accurately by eliminating the interference among subjects.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.20 no.2
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• pp.55-62
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• 2020

A 22-month-old girl who had taken iron supplements due to iron deficiency anemia, presented bloody mucoid stool for one month. She had a bruise at the right periorbital area due to minor trauma and hepatosplenomegaly. Laboratory studies showed anemia, thrombocytopenia, elevated alkaline phosphatase (ALP), hypophosphatemia, decreased haptoglobin, hypocomplementemia, negative direct/indirect Coomb's test, normal vitamin D3 level and high PTHi. Wrist x-ray showed no signs of rickets. The abdominal ultrasound showed only accessory spleen. Tandem mass spectrometry was normal. During follow up, bloody stool regressed after seven days of withdrawal of iron supplement and cow milk, and the total CO2 level had been within 15-20 mEq/L with normal anion gap. NGS (next generation sequencing) panel test for evaluation of renal tubular acidosis showed negative results. After low dose steroid and vitamin D supplements under the impression of hypocomplementemic vasculitis, thrombocytopenia, C3/C4, decreased haptoglobin, and elevated ALP level became normal. At 57 months of age, laboratory findings showed elevated liver enzyme, ALP and gamma-glutamyl transferase again. And liver cirrhosis with splenomegaly and diffuse renal disease were reported with abdomen CT scan. Liver biopsy reported macro- and micronodular cirrhosis. Urine organic acid profile showed elevated succinylacetone level. Whole exome sequencing revealed novel compound heterozygous mutations (NM_00137.2:c.107T>C, NM_00137, 2:c.614T>C) in FAH gene and confirmed by Sanger sequencing. Consequently, the patient was diagnosed as chronic hereditary tyrosinemia type I. She started low phenylalanine/tyrosine diet and nitisinone treatment. Our case had presented symptoms very slowly, which is the first case of chronic tyrosinemia type I in South Korea.

• Journal of Science Education
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• v.45 no.3
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• pp.292-303
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• 2021

This study identifies the terms frequently used together with energy in online science news articles and topics of the news reports to find out how the term energy is used in everyday life and to draw implications for science curriculum and instruction about energy. A total of 2,171 online news articles in science category published by 11 major newspaper companies in Korea for one year from March 1, 2018 were selected by using energy as a search term. As a result of natural language processing, a total of 51,224 sentences consisting of 507,901 words were compiled for analysis. Using the R program, term frequency analysis, semantic network analysis, and structural topic modeling were performed. The results show that the terms with exceptionally high frequencies were technology, research, and development, which reflected the characteristics of news articles that report new findings. On the other hand, terms used more than once per two articles were industry-related terms (industry, product, system, production, market) and terms that were sufficiently expected as energy-related terms such as 'electricity' and 'environment.' Meanwhile, 'sun', 'heat', 'temperature', and 'power generation', which are frequently used in energy-related science classes, also appeared as terms belonging to the highest frequency. From a network analysis, two clusters were found including terms related to industry and technology and terms related to basic science and research. From the analysis of terms paired with energy, it was also found that terms related to the use of energy such as 'energy efficiency,' 'energy saving,' and 'energy consumption' were the most frequently used. Out of 16 topics found, four contexts of energy were drawn including 'high-tech industry,' 'industry,' 'basic science,' and 'environment and health.' The results suggest that the introduction of the concept of energy degradation as a starting point for energy classes can be effective. It also shows the need to introduce high-tech industries or the context of environment and health into energy learning.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.437-446
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• 2022

In April 2020, the Korean government decided to operate the Hwacheon reservoir, a hydropower reservoir to supply water, and it is currently under pilot operation. Through the pilot operation, the Hwacheon reservoir is the first among the hydropower reservoirs in Korea to make a constant release for downstream water supply. In this study, the water supply capacity of the Hwacheon reservoir was estimated using the inflow data of the Hwacheon reservoir. A simulation model was developed to calculate the water supply that satisfies both the monthly water supply reliability of 95% and the annual water supply reliability of 95%. An optimization model was also developed to evaluate the water supply capacity of the Hwacheon reservoir. The inflow data used as input data for the model was modified in two ways in consideration of the impact of the Imnam reservoir. Calculating the water supply for the Hwacheon reservoir using the two modified inflows is as follows. The water supply that satisfies 95% of the monthly water supply reliability is 26.9 m³/sec and 24.1 m³/sec. And the water supply that satisfies 95% of the annual water supply reliability is 23.9 m³/sec and 22.2 m³/sec. Hwacheon reservoir has a maximum annual water supply of 777 MCM (Million Cubic Meter) without failure in the water supply. The Hwacheon reservoir can supply 704 MCM of water per year, considering the past monthly power generation and discharge patterns. If the Hwacheon reservoir performs a routine operation utilizing its water supply capacity, it can contribute to stabilizing the water supply during dry seasons in the Han River Basin.

• (The) Research of the performance art and culture
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• no.38
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• pp.71-109
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• 2019

In this study, succession aspects of direction structure and dancing were reviewed in Moondoong drum dance succeeded by Yong Bae Cho of Goseong Ogwangdae. Sung Rak Hong who succeeded to Moondoong drum dance before Yong Bae Cho directed only 'drum dance' mainly with Goodguri rhythm. While Sung Rak Hong succeeded, the drum of Moondoong Gwangdae became smaller, which was changed from drum before 1965, to semi-drum in 1966 and tabor in 1967 and thereafter. Yong Bae Cho succeeded to Moondoong drum dance since 1970, adding 'Moondoong dance' directing tabor at the floor together with 'drum dance' before August 1972 and directing Dutbaegi rhythm. From the first succession of Goseong Ogwangdae since the winter in 1974, obscene movements were disappeared, and the setting with Yangban and contents to sublimate resentment were added in Moondoong drum dance. These changes seemed to be affected by succession format of Tongyoung Ogwangdae Moondoong drum dance and Ok Jin Gong's idiot dance. There are succession patterns when reviewing Moondoong drum dance directed by Yong Bae Cho. In case of 'Moondoong' dance, repeated forms were succeeded including 'fixed dance'->'impromptu dance'->'jump' in center of three fixed dances. In case of 'drum dance,' repeated forms were succeeded including 'fixed dance'->'rolling tabor'->'concluding' -> 'impromptu dance'->'jump' in center of four fixed dances. In 'drum dance' by Yong Bae Cho, many parts of succeeded dance by Sung Rak Hong who was a prior successor were remained. After Yong Bae Cho's death, Jong Bok Heo summarized the dance with more completed order focusing on the structure of Moondoong drum dance succeeded by Yong Bae Cho. Since then, multiple scenes were added continuously including hobbled appearance by Jong Won Heo, happy scene eating barley and scene to catch tabor stick difficultly, by Chang Ryol Heo. Yong Bae Cho added 'Moondoong dance' to the prior works only with 'drum dance' and started adding the story with resentment. The direction structure summarized by Yong Bae Cho became the basic framework in which the following directors added the scenes very easily. Like this, Yong Bae Cho was an excellent director of Goseong Ogwangdae who inherited Moondoong drum dance from the previous generation to establish and develop to hand over the next generations.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.119-126
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• 2023

Today, in order to improve fuel efficiency and dynamic behavior of automobiles, an era of light weight and simplification of automobile parts is being formed. In order to simplify and design and manufacture the shape of the product, various components are integrated. For example, in order to commercialize three products into one product, product processing is occurring to a very narrow area. In the case of existing parts, precision die casting or casting production is used for processing convenience, and the multi-piece method requires a lot of processes and reduces the precision and strength of the parts. It is very advantageous to manufacture integrally to simplify the processing air and secure the strength of the parts, but if a deep and narrow pocket part needs to be processed, it cannot be processed with the equipment's own spindle. To solve a problem, research on cutting processing is being actively conducted, and multi-axis composite processing technology not only solves this problem. It has many advantages, such as being able to cut into composite shapes that have been difficult to flexibly cut through various processes with one machine tool so far. However, the reality is that expensive equipment increases manufacturing costs and lacks engineers who can operate the machine. In the five-axis cutting processing machine, when producing products with deep and narrow sections, the cycle time increases in product production due to the indirectness of tools, and many problems occur in processing. Therefore, dedicated machine tools and multi-axis composite machines should be used. Alternatively, an angle spindle may be used as a special tool capable of multi-axis composite machining of five or more axes in a three-axis machining center. Various and continuous studies are needed in areas such as processing vibration absorption, low heat generation and operational stability, excellent dimensional stability, and strength securing by using the angle spindle.