• Journal of Bio-Environment Control
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• v.32 no.1
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• pp.23-33
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• 2023

Sucrose (suc) is a disaccharide that consists of glucose (glu) and fructose (fru). It is a carbohydrate source that acts as a nutrient molecule and a molecular signal that regulates gene expression and alters metabolites. This study aimed to evaluate whether suc-specific signaling induces an increase in bioactive compounds by exogenous suc absorption via roots or whether other factors, such as osmotic stress or biotic stress, are involved. To compare the osmotic stress induced by suc treatment, 4-week-old cultured mugwort plants were subjected to Hoagland nutrient solution with 10 mM, 30 mM, and 50 mM of suc or mannitol (man) for 3 days. Shoot fresh weight in suc and man treatments was not significantly different from the control. Both man and suc treatments increased the content of bioactive compounds in mugwort, but they displayed different enhancement patterns compared to the suc treatments. Mugwort extract treated with suc 50 mM effectively protected HepG2 liver cells damaged by ethanol and t-BHP. To compare the biotic stress induced by suc treatment, 3-week-old mugwort plants were subjected to microorganism and/or suc 30 mM with Hoagland nutrient solution. Microorganisms and/or suc 30 mM treatments showed no difference about the shoot fresh weight. However, sugar content in mugwort treated with suc 30 mM and microorganism with suc 30 mM treatment was significantly higher than that of the control. Suc 30 mM and microorganism with suc 30 mM were effective in enhancing bioactive compounds than microorganism treatment. These results suggest that mugwort plants can absorb exogenous suc via roots and the enhancement of bioactive compounds by suc treatment may result not from osmotic stress or biotic stress because of microorganism, but by suc-specific signaling.

• (The) Research of the performance art and culture
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• no.40
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• pp.223-268
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• 2020

In contemporary Theatre, the case of inducing direct communication between the audience and the performance is prominent. Especially with the development of digital technology, the audience wants a personalized experience. The emergence of 'immersive Theatre' in this trend has attracted great attention both at home and abroad. In particular, the most important role in the emergence of the concept of 'immersive Theatre' is the British punchdrunk Theatre. Their representative performance began to premiere in London in the UK in 2003 and has expanded to include New York and China in Shanghai and continues to be extremely popular until 2019. In general, a review of existing studies on the role of the audience in shows that the focus is on the participation of the audience. What experience will be given to the audience can not be emphasized in contemporary Theatre. In order to satisfy the diverse needs of the audience, contemporary Theatre are increasingly showing complexity that cannot be explained by any one theory. The same goes for . This is because each audience wants a personalized experience, and there are differences in experience depending on the environment in which the audience also grew up, knowledge, culture, and taste. This study selected Punch Drunk's as a performance that can represent contemporary Theatre, and conducted a study on the role of audience in contemporary Theatre. To this end, we have historically explored past discussions about the role of the audience and discussed the characteristics of the role of the audience in contemporary Theatre. Next, I analyzed in detail the experience of the researcher "He" who watched the performance with the researcher on the role of the audience in . In conclusion, the experience of the audience in is diverse and complex. In other words, the role of the traditional audience in the proscenium play, as well as the audience as a participant in the post-drama play, was also complex in the performance. And this complexity was not a coincidence, but a planning strategy for the Punchdrunk Theatre. Therefore, when discussing the role of the audience in contemporary Theatre, there should be a discussion that clearly sees the complex characteristics of contemporary Theatre through the approach from various perspectives, rather than merely one view of the audience as a participant. something to do.

• Food Science and Preservation
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• v.31 no.1
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• pp.112-125
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• 2024

This study determined the relationships between the total anthocyanin content in apple skin and the total polyphenols, flavonoids, proanthocyanidins, and soluble solids contents in the flesh of cold-stored Fuji apples. Total anthocyanin content in apple skin ranged from 0.130±0.005 mg CE/g fw to 0.262±0.028 mg CE/g fw, and the overall average was 0.200±0.008 mg CE/g fw. The total polyphenolic compounds in the flesh was ranged from a 4.283±0.141 mM GAE/g fw to 8.207±0.234 mM GAE/g fw, and the average content was 6.275±0.177 mM GAE/g fw. The total flavonoid content ranged from 4.510±0.080 mM QE/g fw to 2.467±0.458 mM QE/g fw, and the average total flavonoid content was about 3.586 mM QE/g fw. The total proanthocyanidin content was relatively high, ranging from 3.475±0.577 mM EE/g fw to 6.816±0.277 mM EE/g fw, and the soluble solid in the flesh was about 12 °Brix to 14 °Brix. The DPPH radical scavenging activity of extracts from apple flesh ranged from 66.36% to 94.99%, and the ascorbate equivalent concentration was 0.482 mM. The ABTS radical scavenging activity was 99.12% to 99.9%, indicating a higher inhibitory activity than the DPPH inhibitory activity, and the ascorbate equivalent concentration was 0.486 mM. The correlation between the total anthocyanin and total polyphenolic compounds was y = 15.192x + 3.2169 (R²=0.2748), but the concentration of total polyphenolic compounds increased when the total anthocyanin content was increased. The correlation equation of total anthocyanin with total flavonoids was y = 15.18x + 0.5555 (R²=0.6226), with total proanthocyanin was y = 14.918x + 2.3422 (R²=0.3372), and with soluble solid was y = 10.558x + 11.126 (R²=0.1925), indicating that the correlation of total anthocyanin with total flavonoid was higher than that with soluble solid.

• Journal of Life Science
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• v.34 no.1
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• pp.48-58
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• 2024

Salmonella is a common food-borne intracellular bacterial pathogen that has triggered significant public health concerns. Salmonella hosts' genetic factors play a pivotal role in determining their susceptibility to the pathogen. Cysteine-rich intestinal protein 1 (CRIP1), a member of LIM/double zinc finger protein family, is widely expressed in humans, such as in the lungs, spleen, and especially the gut. Recently, CRIP1 has been reported as a key marker of several immune disorders; however, the effect of CRIP1 on bacterial infection remains unknown. We aimed to elucidate the relationship between Salmonella infection and CRIP1 gene deficiency, as Salmonella spp. is known to invade the Peyer's patches of the small intestine, where CRIP1 is highly expressed. We found that CRIP1-deficient conditions could not alter the characteristics of bone marrow-derived myeloid cells in terms of phagocytosis on macrophages and the activation of costimulatory molecules on dendritic cells using ex vivo differentiation. Moreover, flow cytometry data showed comparable levels of MHCII⁺CD11b⁺CD11c⁺ dendritic cells and MHCII⁺F4/80⁺CD11b⁺ macrophages between WT and CRIP1 knockout (KO) mice. Interestingly, the basal population of monocytes in the spleen and neutrophils in MLNs is more abundant in a steady state of CRIP1 KO mice than WT mice. Here, we demonstrated that the CRIP1 genetic factor plays dispensable roles in host susceptibility to Salmonella Typhimurium infections and the activation of myeloid cells. In addition, differential immune cell populations without antigen exposure in CRIP1 KO mice suggest that the regulation of CRIP1 expression may be a novel immunotherapeutic approach to various infectious diseases.

• The Journal of the Petrological Society of Korea
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• v.8 no.2
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• pp.106-124
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• 1999

In the Cheongju-Minwon area which occupies the middle part of the Ogcheon Metamorphic Belt, three metamorphic events(M1, M2, M3) had occurred. Intermediate P/T type M2 regional metamorphism formed prevailing mineral assemblages in the study area. Low PIT type M3 contact metamorphism occurred due to the intrusion of granites after M2 metamorphism. M1 metamorphism is recognized by inclusions within garnet. During M2 metamorphism, the metamorphic grade increased from the biotite zone in the southeastern part to the garnet zone in the northwestern part of the study area. This result is similar to the metamorphic evolution of the southwestern part of the Ogcheon Metamorphic Belt. Garnets in the garnet zone are classified into two types; Type A garnet has inclusions whose trail is connected to the foliation in the matrix and Type B garnet has inclusion rich core and inclusion poor rim. Type A garnet formed in the mica rich part with crenulation cleavage whereas Type B garnet formed in the quartz rich part with weak crenulation cleavage. In some outcrops, two types garnets are found together. Compared to the rim of Type A garnet, the rim of Type B garnet is lower in grossular and spessartine contents but higher in almandine and pyrope contents. In some Type B garnets, the inclusion poor part is rimmed by muddy colored or protuberant new overgrowth. In the inclusion poor part and new overgrowth, a rapid increase in grossular and decrease in spessartine is observed. However, the compositional patterns of Type A and B are similar; Ca increases and Mn decreases from core to rim. Two types garnets formed mainly due to the difference of bulk chemistry instead of metamorphic and deformational differences. The metamorphic P-T conditions estimated from Type A garnets are 595-690 OC15.7-8.8 kb, which indicates M2 metamorphism is intermediate P/T type metamorphism. On the other hand, a wide range of P-T conditions is calculated from Type B garnets. The P-T conditions from most Type B garnet rims are 617-690 OC16.2-8.9 kb which also indicates an intermediate P/T type metamorphism. However, at the rim part with flat end or weak overgrowth, grossular content is low and 573-624OC14.7-5.8 kb are estimated. The P-T conditions calculated from plagioclase and biotite inclusions in garnet are 460-500 0C/1.9-3.0 kb. The P-T conditions from rim part with weak overgrowth and inclusions within garnet, indicate that low P/T type M1 regional metamorphism might have occurred before intermediate P/T type M2 regional metamorphism. The P-T conditions estimated from samples which had undergone low PIT type M3 metamorphism strongly, are 547-610 0C/2.1-5.0 kb.

• Economic and Environmental Geology
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• v.12 no.3
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• pp.147-176
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• 1979

The Yeonhwa II zinc-lead mine is characterized by a dozen of moderately dipping tabular orebodies of skarn and zinc-lead sulfides, developed in accordance with the ENE-trending bedding thrusts and bedding planes of the Pungchon Limestone and underlying Myobong Formation, mostly along the contacts of a ENE-trending sill and a NW-trending dike of quartz mononite porphyry. The orebodies occur in three groups: (1) the footwall Wolgok orebodies with respect to the sill, (2) the hangingwall Wolgok orebodies, and (3) the Seongok orebodies extended from dike contacts into carbonate beds. Mineral compositions of these orebodies are dominated by calc-silicates (skarn) associated with ore minerals of sphalerite, galena, and chalcopyrite, as well as sulfide gangue of pyrrhotite. A pair of exo- and endo-skerns in the Wolgok footwall contact aureole between the Pungchon Limestone and quartz monzonite porphyry on the -120 level represents a well-developed symmetrical pattern of mineral zoning: a garnet/quartz zone in the center of exoskarn, two zones of pyroxene with ore minerals on both sides of the garnet/quartz zone, further outwards-an epidote/chlorite-bearing hornfelsic zone in the Myobong slate beyond a zone of unaffected limestone, and an epidote-dominated zone of endo skarn on the opposite side toward fresh quartz monzonite porphyry. These features indicate a combination of two effects on the skarn formation: (1) differences in composition of the host rocks(sedimentary and ignous), and (2) progressive outward migration of inner zones on outer zones on the course of metasomatic replacement of the pre-existing minerals. Microprobe analyses of garnet, pyroxene, pyroxenoids, epidote, and chlorite for nine major elements on a total of 23 mineral grains revealed that: the pyroxenes are hedenbergitic, in most zones, with a gradual decrease of Fe- and Mn-contents toward the central zone, whereas the garnets are andraditic in outer zones, but are grossularitic in the central zone. This indicates a reverse relationship of Fe-contents between pyroxene and garnet across the exoskarn zones. Pyroxenoids are lacking in wollastonite but are dominated by pyroxmangite, rhodonite and bustamite, indicating a Mn-rich nature in bulk chemistry. Pseudomorphic fluorite after garnet occurs abundantly reflecting a fluorine-enhanced evidence of the skarn-forming fluids. Epidote contains 0.19-0.25mole fraction of pistacite, and chlorite is Mn-rich but is Mg-poor. Sulfide mineralization took place with the most Fe-rich pyroxene rather than with garnet as indicated by the fact that the highest value of hedenbergite mole fraction occurs in the ore-bearing pyroxene zone. The Yeonhwa II ores are characterized by high zinc and low lead in metal grade, with minor quantity of copper content in almost constant grade. The hangingwall Wolgok and Seongok orebodies, that formed in a more open environment with respect to their local configurations of geologic setting, are more variable in metal grades and ratios, than are the footwall Wolgok orebodies formed in a more closed condition in a narrow interval of sedimentary beds.

• Economic and Environmental Geology
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• v.40 no.5
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• pp.635-649
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• 2007

Geochemical composition, stable isotopes $({\delta}^{18}O,\;{\delta}D,\;{\delta}^{34}S)$ and noble gases(He, Ne and Ar) of nine hot spring water and three groundwater for five hot springs(Jukam, Hwasun, Dokog, Jirisan, Beunsan) from the Honam area were analyzed to investigate the hydrogeochemical characteristics and the hydrogeochemical evolution of the hot spring waters, and to interpret the source of sulfur, helium and argon dissolved in the hot spring waters. The hot spring waters show low water temperature ranging from 23.0 to $30.5^{\circ}C$ and alkaline characteristics of pH 7.67 to 9.98. Electrical conductivity of hot spring waters is $153{\sim}746{\mu}S/cm$. Groundwaters in this area were characterized by the acidic to neutral pH range$(5.85{\sim}7.21)$, the wide electrical conductivity range $(44{\sim}165{\mu}S/cm)$. The geochemical compositions of hot spring and groundwaters can be divided into three water types: (1) $Na-HCO_3$ water type, (2) Na-Cl water type and (3) $Ca-HCO_3$ water type. The hot spring water of $Ca-HCO_3$ water type in early stage have been evolved through $Ca(Na)-HCO_3$ water type into $Na-HCO_3$ type in final stage. In particular, Jurim alkaline(pH 9.98) hot spring water plotted at the end point of $Na-HCO_3$ type in the Piper diagram is likely to arrive into the final stage in geochemical evolution process. Hydrogen and oxygen isotopic data of the hot spring water samples indicate that the hot spring waters originated from the local meteoric water showing latitude and altitude effects. The ${\delta}^{34}S$ value for sulfate of the hot spring waters varies widely from 0.5 to $25.9%o$. The sulfur source of most hot spring waters in this area is igneous origin. However, The ${\delta}^{34}S$ also indicates the sulfur of JR1 hot water is originated from marine sulfur which might be derived ken ancient seawater sulfates. The $^3He/^4He\;and\;^4He/^{20}Ne$ ratios of the hot spring waters range from $0.0143{\times}10^{-6}\;to\;0.407{\times}10^{-6}\;and\;6.49{\sim}584{\times}10^{-6}$, respectively. The hot spring waters are plotted on the mixing line between air and crustal components. It means that the He gas in the hot spring waters was mainly originated from crustal sources. However, the JR1 hot spring water show a little mixing ratio of the helium gas of mantle source. The $^{40}Ar/^{36}Ar$ ratios of hot spring water are in the range from $292.3{\times}10^{-6}\;to\;304.1{\times}10^{-6}$, implying the atmospheric argon source.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.109-125
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• 2019

As interest on intelligent search engines increases, various studies have been conducted to extract and utilize the features related to products intelligencely. In particular, when users search for goods in e-commerce search engines, the 'color' of a product is an important feature that describes the product. Therefore, it is necessary to deal with the synonyms of color terms in order to produce accurate results to user's color-related queries. Previous studies have suggested dictionary-based approach to process synonyms for color features. However, the dictionary-based approach has a limitation that it cannot handle unregistered color-related terms in user queries. In order to overcome the limitation of the conventional methods, this research proposes a model which extracts RGB values from an internet search engine in real time, and outputs similar color names based on designated color information. At first, a color term dictionary was constructed which includes color names and R, G, B values of each color from Korean color standard digital palette program and the Wikipedia color list for the basic color search. The dictionary has been made more robust by adding 138 color names converted from English color names to foreign words in Korean, and with corresponding RGB values. Therefore, the fininal color dictionary includes a total of 671 color names and corresponding RGB values. The method proposed in this research starts by searching for a specific color which a user searched for. Then, the presence of the searched color in the built-in color dictionary is checked. If there exists the color in the dictionary, the RGB values of the color in the dictioanry are used as reference values of the retrieved color. If the searched color does not exist in the dictionary, the top-5 Google image search results of the searched color are crawled and average RGB values are extracted in certain middle area of each image. To extract the RGB values in images, a variety of different ways was attempted since there are limits to simply obtain the average of the RGB values of the center area of images. As a result, clustering RGB values in image's certain area and making average value of the cluster with the highest density as the reference values showed the best performance. Based on the reference RGB values of the searched color, the RGB values of all the colors in the color dictionary constructed aforetime are compared. Then a color list is created with colors within the range of ${\pm}50$ for each R value, G value, and B value. Finally, using the Euclidean distance between the above results and the reference RGB values of the searched color, the color with the highest similarity from up to five colors becomes the final outcome. In order to evaluate the usefulness of the proposed method, we performed an experiment. In the experiment, 300 color names and corresponding color RGB values by the questionnaires were obtained. They are used to compare the RGB values obtained from four different methods including the proposed method. The average euclidean distance of CIE-Lab using our method was about 13.85, which showed a relatively low distance compared to 3088 for the case using synonym dictionary only and 30.38 for the case using the dictionary with Korean synonym website WordNet. The case which didn't use clustering method of the proposed method showed 13.88 of average euclidean distance, which implies the DBSCAN clustering of the proposed method can reduce the Euclidean distance. This research suggests a new color synonym processing method based on RGB values that combines the dictionary method with the real time synonym processing method for new color names. This method enables to get rid of the limit of the dictionary-based approach which is a conventional synonym processing method. This research can contribute to improve the intelligence of e-commerce search systems especially on the color searching feature.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.1-22
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• 2021

Aspect Based Sentiment Analysis (ABSA), which analyzes sentiment based on aspects that appear in the text, is drawing attention because it can be used in various business industries. ABSA is a study that analyzes sentiment by aspects for multiple aspects that a text has. It is being studied in various forms depending on the purpose, such as analyzing all targets or just aspects and sentiments. Here, the aspect refers to the property of a target, and the target refers to the text that causes the sentiment. For example, for restaurant reviews, you could set the aspect into food taste, food price, quality of service, mood of the restaurant, etc. Also, if there is a review that says, "The pasta was delicious, but the salad was not," the words "steak" and "salad," which are directly mentioned in the sentence, become the "target." So far, in ABSA, most studies have analyzed sentiment only based on aspects or targets. However, even with the same aspects or targets, sentiment analysis may be inaccurate. Instances would be when aspects or sentiment are divided or when sentiment exists without a target. For example, sentences like, "Pizza and the salad were good, but the steak was disappointing." Although the aspect of this sentence is limited to "food," conflicting sentiments coexist. In addition, in the case of sentences such as "Shrimp was delicious, but the price was extravagant," although the target here is "shrimp," there are opposite sentiments coexisting that are dependent on the aspect. Finally, in sentences like "The food arrived too late and is cold now." there is no target (NULL), but it transmits a negative sentiment toward the aspect "service." Like this, failure to consider both aspects and targets - when sentiment or aspect is divided or when sentiment exists without a target - creates a dual dependency problem. To address this problem, this research analyzes sentiment by considering both aspects and targets (Target-Aspect-Sentiment Detection, hereby TASD). This study detected the limitations of existing research in the field of TASD: local contexts are not fully captured, and the number of epochs and batch size dramatically lowers the F1-score. The current model excels in spotting overall context and relations between each word. However, it struggles with phrases in the local context and is relatively slow when learning. Therefore, this study tries to improve the model's performance. To achieve the objective of this research, we additionally used auxiliary loss in aspect-sentiment classification by constructing CNN(Convolutional Neural Network) layers parallel to existing models. If existing models have analyzed aspect-sentiment through BERT encoding, Pooler, and Linear layers, this research added CNN layer-adaptive average pooling to existing models, and learning was progressed by adding additional loss values for aspect-sentiment to existing loss. In other words, when learning, the auxiliary loss, computed through CNN layers, allowed the local context to be captured more fitted. After learning, the model is designed to do aspect-sentiment analysis through the existing method. To evaluate the performance of this model, two datasets, SemEval-2015 task 12 and SemEval-2016 task 5, were used and the f1-score increased compared to the existing models. When the batch was 8 and epoch was 5, the difference was largest between the F1-score of existing models and this study with 29 and 45, respectively. Even when batch and epoch were adjusted, the F1-scores were higher than the existing models. It can be said that even when the batch and epoch numbers were small, they can be learned effectively compared to the existing models. Therefore, it can be useful in situations where resources are limited. Through this study, aspect-based sentiments can be more accurately analyzed. Through various uses in business, such as development or establishing marketing strategies, both consumers and sellers will be able to make efficient decisions. In addition, it is believed that the model can be fully learned and utilized by small businesses, those that do not have much data, given that they use a pre-training model and recorded a relatively high F1-score even with limited resources.