• The Journal of the Convergence on Culture Technology
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• v.5 no.3
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• pp.271-282
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• 2019

This study was conducted to evaluate physiological activity of Robinia pseudo-acacia leaf and its skin penetration using polymer micelles and skin penetrating peptide. After extraction with Robinia pseudo-acacia using the ethanol and distilled water, various physiological activities were examined. The total concentration of polyphenol compounds was determined to be 47.42 mg/g (ethanol extract), 56.88 mg/g (hydrothermal extract) and DPPH radical scavenging ability at $1,000{\mu}g/mL$ was 44.24% in ethanol extract and it is higher than value(41.50%) in hydrothermal extract. The elastase inhibitory assay showed concentration dependence and elastase inhibition of Robinia pseudo acacia leaf ethanol extract was 54.09%, which was the highest at $500{\mu}g/mL$. In the SOD-like experiments, the concentration-dependent results were showed and the SOD-like activity of the Robinia pseudo-acacia leaf ethanol extract was higher than that of the Robinia pseudo acacia leaf hydrothermal extract at all concentrations. At a concentration of $500{\mu}g/mL$, Robinia pseudo acacia leaf ethanol extract showed the highest SOD-like activity of 76.41%. The tyrosinase inhibition at $20{\mu}g/mL$ was determined to be 56.47% (ethanol extract), 23.05% (hydrothermal extract). In the antimicrobial experiments, the hydrothermal extract had no effect, but ethanol extract represented maximum clear zone of 11.00 mm in Propionbacterium acnes strain and maximum clear zone of 10.50 mm. in Bacillus subtilis strain. To solve the problem of insolubility and to improve skin penetration, PCL-PEG polymer micelles containing Robinia pseudo-acacia leaf ethanol extracts and 1.0% cell permeable peptide, hexa-D-arginine (R6) were successfully prepared with particle size of 108.23 and 126.47 nm and excellent skin permeation effects could be showed.

• Journal of Nutrition and Health
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• v.54 no.2
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• pp.211-223
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• 2021

Purpose: A total of 39 seafood menus were prepared according to the Korean standard recipe, and analyzed for vitamin B₉ (folate) and B₁₂ (cobalamins) contents, using validated applied analytical methods. The menus included Guk/Tang/Jjigae (boiled or stewed dishes, n = 10), Bokkeum (stir-fried dishes, n = 10), Jjim/Jorim (braised or steamed dishes, n = 7), Gui (baked or grilled dishes, n = 7), Twigim (deep-fried dishes, n = 2) and Muchim (dried or blanched-seasoned dishes, n = 3). Methods: The contents of vitamin B₉ and B₁₂ in all food samples were determined by the trienzyme extraction-Lactobacillus casei and immunoaffinity-high-performance liquid chromatography/photodiode array detection methods. Analytical quality control was performed in order to assure reliability of the analysis. Results: Accuracy (97.4-100.6% recoveries) and precision (< 6% relative standard deviations for repeatability and reproducibility) of vitamin B₉ and B₁₂ analyses were determined to be excellent. The vitamin B₉ and B₁₂ contents of the 39 seafood menus evaluated, varied in the range of 1.83-523.08 ㎍/100 g and 0.11-38.30 ㎍/100 g, respectively, depending on the ingredients and cooking methods. The vitamin B₉ content was highest in Jomi-gim (523.08 ㎍/100 g), followed by Geonsaeu-bokkeum (128.34 ㎍/100 g) and Janmyeolchi-bokkeum (121.53 ㎍/100 g). Vitamin B₁₂ was detected in all seafood menus, with highest level obtained in Kkomack-jjim (41.58 ㎍/100 g). The seaweed dish was found to have high levels of both vitamin B₉ and B₁₂. All assays were performed under strict quality control. Conclusion: Guk and Tang menus, which contain a large amount of water, were relatively lower in the vitamin B₉ and B₁₂ contents than the other menus. Bokkeum menus containing various vegetables were high in the vitamin B₉ content, but the vitamin B₁₂ content was dependent on the type of seafood used in the menu.

• Korean Journal of Mineralogy and Petrology
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• v.35 no.3
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• pp.333-344
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• 2022

The quarrying and utilization of natural building stones such as granite and marble are rapidly emerging in developing countries. A huge amount of wastes is being generated during the processing, cutting and sizing of these stones to make them useable. These wastes are disposed of in the open environment and the toxic nature of these wastes negatively affects the environment and human health. The growth trend in the world stone industry was confirmed in output for 2019, increasing more than one percent and reaching a new peak of some 155 million tons, excluding quarry discards. Per-capita stone use rose to 268 square meters per thousand persons (m²/1,000 inh), from 266 the previous year and 177 in 2001. However, we have to take into consideration that the world's gross quarrying production was about 316 million tons (100%) in 2019; about 53% of that amount, however, is regarded as quarrying waste. With regards to the stone processing stage, we have noticed that the world production has reached 91.15 million tons (29%), and consequently this means that 63.35 million tons of stone-processing scraps is produced. Therefore, we can say that, on a global level, if the quantity of material extracted in the quarry is 100%, the total percentage of waste is about 71%. This raises a substantial problem from the environmental, economical and social point of view. There are essentially three ways of dealing with inorganic waste, namely, reuse, recycling, or disposal in landfills. Reuse and recycling are the preferred waste management methods that consider environmental sustainability and the opportunity to generate important economic returns. Although there are many possible applications for stone waste, they can be summarized into three main general applications, namely, fillers for binders, ceramic formulations, and environmental applications. The use of residual sludge for substrate production seems to be highly promising: the substrate can be used for quarry rehabilitation and in the rehabilitation of industrial sites. This new product (artificial soil) could be included in the list of the materials to use in addition to topsoil for civil works, railway embankments roundabouts and stone sludge wastes could be used for the neutralization of acidic soil to increase the yield. Stone waste is also possible to find several examples of studies for the recovery of mineral residues, including the extraction of metallic elements, and mineral components, the production of construction raw materials, power generation, building materials, and gas and water treatment.

• Journal of Life Science
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• v.31 no.11
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• pp.1010-1018
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• 2021

Although varieties of silkworms, which have recently attracted attention as a health functional food, are being produced, studies on the differences in the functionality of different silkworm varieties are insufficient. Therefore, in this study, the antioxidant activities of different silkworm breeds bred in domestic farms were analyzed, and the potential for their cultivation as specialized varieties with excellent antioxidant function was investigated. To compare antioxidant activity, four varieties of silkworms, white Bakokjam, Golden silk, Yeonokjam, and Hanseongjam, were bred; water and ethanol extracts of these silkworms were prepared on the 3rd and 5th days of the 5th instar larval stage. The highest extraction yield was seen for the water extract from the Golden silk variety on the 3rd day of the 5th instar; the highest total phenolic compound and flavonoid contents were observed for the water extract (86.11±4.04 ㎍/mg GE) and 70% (v/v) ethanol extract (46.70±2.81 ㎍/mg QE). Bakokjam and Yeonokjam exhibited DPPH radical scavenging activity of up to 78% and showed the highest nitrite scavenging activity (85%) at pH 1.2. The maximum SOD-like activity of Yeonokjam was about 47%. Furthermore, 48 ㎍/ml of the Yeonokjam extract showed a reducing power of 0.7 abs, which was the best among the four varieties. Considering these results, the Yeonokjam (on the 3rd day of the 5th instar) had antioxidant activity and represents a silkworm cultivar that would be suitable for cultivation as a health food.

• The Journal of Engineering Geology
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• v.33 no.1
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• pp.85-103
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• 2023

The in-situ remediation of a solidified stratum containing a large amount of fine-texture material like clay or organic matter in contaminated soil faces limitations such as increased remediation cost resulting from decreased purification efficiency. Even if the soil conditions are good, remediation generally requires a long time to complete because of non-uniform soil properties and low permeability. This study assessed the remediation effect and evaluated the field applicability of a methodology that combines pneumatic fracturing, vacuum extraction, and plasma blasting (the PPV method) to improve the limitations facing existing underground remediation methods. For comparison, underground remediation was performed over 80 days using the experimental PPV method and chemical oxidation (the control method). The control group showed no decrease in the degree of contamination due to the poor delivery of the soil remediation agent, whereas the PPV method clearly reduced the degree of contamination during the remediation period. Remediation effect, as assessed by the reduction of the highest TPH (Total Petroleum Hydrocarbons) concentration by distance from the injection well, was uncleared in the control group, whereas the PPV method showed a remediation effect of 62.6% within a 1 m radius of the injection well radius, 90.1% within 1.1~2.0 m, and 92.1% within 2.1~3.0 m. When evaluating the remediation efficiency by considering the average rate of TPH concentration reduction by distance from the injection well, the control group was not clear; in contrast, the PPV method showed 53.6% remediation effect within 1 m of the injection well, 82.4% within 1.1~2.0 m, and 68.7% within 2.1~3.0 m. Both ways of considering purification efficiency (based on changes in TPH maximum and average contamination concentration) found the PPV method to increase the remediation effect by 149.0~184.8% compared with the control group; its average increase in remediation effect was ~167%. The time taken to reduce contamination by 80% of the initial concentration was evaluated by deriving a correlation equation through analysis of the TPH concentration: the PPV method could reduce the purification time by 184.4% compared with chemical oxidation. However, the present evaluation of a single site cannot be equally applied to all strata, so additional research is necessary to explore more clearly the proposed method's effect.

• Journal of Korean Society of Forest Science
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• v.112 no.1
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• pp.40-56
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• 2023

Tree species within the Populus genus grow rapidly and have an excellent capacity to absorb carbon, conferring substantial ability to effective purify the environment. Poplar breeding can be achieved rapidly and efficiently if a genetic linkage map is constructed and quantitative trait loci (QTLs) are identified. Here, a high-density genetic linkage map was constructed for the control pollinated progeny using the genotyping-by-sequencing (GBS) technique, which is a next-generation sequencing method. A search was also performed for the genes associated with quantitative traits located in the genetic linkage map by examining the variables of height and diameter at root collar, and resilience to insect damage. The height and diameter at root collar were measured directly, while the ability to recover from insect damage was scored in a 4-year-old breeding population of aspen hybrids (Odae19 × Bonghyeon4 F1) established in the research forest of Seoul National University. After DNA extraction, paternity was confirmed using five microsatellite markers, and only the individuals for which paternity was confirmed were used for the analysis. The DNA was cut using restriction enzymes and the obtained DNA fragments were prepared using a GBS library and sequenced. The analyzed results were sorted using Populus trichocarpa as a reference genome. Overall, 58,040 aligned single-nucleotide polymorphism (SNP) markers were identified, 17,755 of which were used for mapping genetic linkages. The genetic linkage map was divided into 19 linkage groups, with a total length of 2,129.54 cM. The analysis failed to identify any growth-related QTLs, but a gene assumed to be related to recovery from insect damage was identified on linkage group (chromosome) 4 through genome-wide association study.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.50 no.2
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• pp.179-192
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• 2024

In this study, six types of natural products, Prunus tomentosa (P. tomentosa), Akebia quinata (A. quinata), Prunus armeniaca (P. armeniaca), Smallanthus sonchifolius (S. sonchifolius), Citrus japonica (C. japonica), and Citrus australasica (C. australasica), were used to verify the effect of improving sleep and skin barriers by stress relief. As a result of the experiment, the production of cortisol, a stress hormone, was significantly inhibited by the P. tomentosa, C. australasica, A. quinata, and C. japonica among the six natural products. In addition, the expression of GAD67, a GABA-producing enzyme involved in sleep regulation, showed a significant increase in P. tomentosa purified water extract and C. australasica 50% ethanol extract, and the extract by each P. tomentosa solvent was found to have the highest total polyphenol content. Based on the results, the P. tomentosa extract with the highest activity was finally selected, and subsequent experiments were conducted. Among each P. tomentosa solvent extract, the DPPH radical scavenging activity was the highest in the 30% ethanol extract, and purified water extract increased GABA production and skin barrier factors filaggrin and claudin-1 expression the highest. HPLC analysis confirmed quercitrin as the main component of P. tomentosa extract, and quercitrin content by extraction solvent was high in the order of 30% ethanol > purified water > 70% ethanol > 50% ethanol. Quercitrin inhibited the production of cortisol in a concentration-dependent manner, significantly increasing GAD67 expression and GABA production, which had been reduced by cortisol. From the results of this study, it has been demonstrated that P. tomentosa can be used as a cosmetic material to help improve sleep and strengthen skin barriers by relieving stress.

• Korean Journal of Environment and Ecology
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• v.38 no.3
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• pp.230-245
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• 2024

Amphibians, sensitive to external environmental changes, serve as bioindicator species for assessing alterations or disturbances in local ecosystems. It is known that one-third of amphibian species within the order Anura are at risk of extinction due to anthropogenic threats such as habitat destruction and fragmentation caused by urbanization. To develop effective protection and conservation strategies for anuran amphibians, species surveys that account for population characteristics are essential. This study aimed to investigate the potential for citizen participation in ecological monitoring using the mating calls of anura species. We also proposed suitable quality control measures to mitigate errors and biases, ensuring the extraction of reliable species occurrence data. The Citizen Science project was carried out nationwide from April 1 to August 31, 2022, targeting 12 species of anura amphibians in Korea. Citizens voluntarily participated in voice signal monitoring, where they listened to anura species' mating calls and recorded them using a mobile application. Additionally, we established a quality control process to extract reliable species occurrence data, categorizing errors and biases from citizen-collected data into three levels: omission, commission, and incorrect identification. A total of 6,808 observations were collected during the citizen participation in anura species vocalization monitoring. Through the quality control process, errors and biases were identified in 1,944 (28.55%) of the 6,808 data. The most common type of error was omission, accounting for 922 cases (47.43%), followed by incorrect identification with 540 cases (27.78%), and commission with 482 cases (24.79%). During the Citizen Science project, we successfully recorded the mating calls of 10 out of the 12 anuran amphibian species in Korea, excluding the Asian toads (Bufo gargarizans Cantor), Korean brown frog (Rana coreana). Difficulties in collecting mating calls were primarily attributed to challenges in observing due to population decline or discrepancies between the breeding season of non-emergent individuals and the timing of the citizen science project. This study represents the first investigation of distribution status and species emergence data collection through mating calls of anura species in Korea based on citizen participation. It can serve as a foundation for designing future bioacoustic monitoring that incorporates citizen science and quality control measures for citizen science data.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.