• Journal of agriculture & life science
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• v.44 no.6
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• pp.159-170
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• 2010

This study was aimed to evaluate the effects garlic such as fresh garlic powder (FGP), red garlic powder (RGP) and black garlic powder (BGP) by lyophilized, on serum lipid metabolism in obese rats induced high fat diet. Food efficiency ratio (FER) of the experimental groups was lower than the control group and it was significant difference. Total lipid content of serum decreased from 7.0 to 20.9% compared to the control group. Especially, triglyceride content decreased from 40.8% (BGP) to 42.1% (RGP) group as compared to the control group. There was no significant difference in HDL-C content between groups fed garlic powder and normal group. LDL-C contents of the experimental groups were lower than the control group, but has not showed significant diggerence compared to control group. Total lipid content was significantly increased in liver compared to the control group, but decreased over 30% in RGP and BGP groups to the control group. The lipid level in feces was increased by feeding periods of garlic powder, while total cholesterol and triglyceride were significantly increased in feces of RGP group. TBARS content in serum and liver of RGP and BGP groups was significantly decreased than the control group. Antioxidant activity of serum was 68.05% in the RGP group, which was significantly higher than the control group. From the above result, we suggested that red and black garlic powder were effective in the improvement of lipid level in obese rats induced high fat diet.

• Journal of the Korean Society of Marine Environment & Safety
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• v.23 no.6
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• pp.719-730
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• 2017

Recently, oceanic research has been carried out investigating global scientific interests and the territorial management of national marine jurisdictional waters, including exclusive economic zones (EEZ) and the open seas. To meet the needs of ocean researchers pursuing these - objectives, acquiring advanced research infrastructure, including research vessels, large facilities, and equipment, is a top priority in ocean science. However, ocean science is a similar to space science, and securing resources and state-of-the-art technology can be expensive. Faced with these challenges, our study focused on establishing a strategy for the efficient operation and management of research vessels, attempting to establish benchmarks from foreign examples that can be adapted to suit the target context. The results of this study provide ways to identify operating systems that could increase the efficiency of joint-use research vessels. The different systems examined in this study included a joint-use committee-based management system (JCMS, Type 1), private enterprise entrusted operating system (PEOS, Type 2), institutional investment operating system (IIOS, Type 3), and commissioned executive operating system (CEOS, Type 4). The efficiencies of JCMS, PEOS, IIOS and CEOS were 9.17, 5.82, 11.2 and -1.72 %, respectively. Given the total costs involved, the most affordable operating system was IIOS. JCMS was the most cost-effective system based on a quantitative cost-benefit analysis, but IIOS also had an acceptable cost-benefit balance. An operational committee would be required and regulations and guidelines shoulde be established to employ, JCMS, while a strategy to yield independent revenue would be needed to utilize an IIOS system.

• Korean Journal of Ecology and Environment
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• v.54 no.3
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• pp.199-208
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• 2021

Environmental DNA (eDNA) metabarcoding is effective method with high detection sensitivity for evaluating fish biodiversity and detecting endangered fish from natural water samples. We compared the richness of operational taxonomic units(OTUs) and composition of freshwater fishes according to filters(cellulose nitrate filter vs. glass fiber filter), extraction kits(DNeasy2^® Blood & Tissue Kit vs. DNeasy2^® PowerWater Kit), primer sets (12S rDNA vs. 16S rDNA), and PCR methods (conventional PCR vs. touchdown PCR) to determine the optimal conditions for metabarcoding analysis of Korean freshwater fish. The glass fiber filter and DNeasy2^® Blood & Tissue Kit combination showed the highest number of freshwater fish OTUs in both 12S and 16S rDNA. Among the four types, the primer sets only showed statistically significant difference in the average number of OTUs in class Actinopterygii (non-parametric Wilcoxon signed ranks test, p=0.005). However, there was no difference in the average number of OTUs in freshwater fish. The species composition also showed significant difference according to primer sets (PERMANOVA, Pseudo-F=6.9489, p=0.006), but no differences were observed in the other three types. The non-metric multidimensional scaling (NMDS) results revealed that species composition clustered together according to primer sets based on similarity of 65%; 16S rDNA primer set was mainly attributed to endangered species such as Microphysogobio koreensis and Pseudogobio brevicorpus. In contrast, the 12S rDNA primer set was mainly attributed to common species such as Zacco platypus and Coreoperca herzi. This study provides essential information on species diversity analysis using metabarcoding for environmental water samples obtained from rivers in Korea.

• Food Engineering Progress
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• v.15 no.4
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• pp.388-397
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• 2011

The purpose of this study was to determine the optimum ethanol extraction conditions for maximum extraction of functional components such as ferulic acid, oryzanol, and toopherol from black rice bran using Response Surface Methodology (RSM). A central composite design was applied to investigate the effects of the independent variables of solvent ratio ($X_{1}$), extraction temperature ($X_{2}$) and extraction time ($X_{3}$) on the dependent variables such as total phenol components ($Y_{1}$), total flavonoids compounds ($Y_{2}$), electron donating ability ($Y_{3}$), $\gamma$-oryzanol ($Y_{4}$), ferulic acid ($Y_{5}$) and $\alpha$-toopherol components ($Y_{6}$). ANOVA results showed that coefficients of determination (R-square) of estimated models for dependent variables ranged from 0.8939 to 0.9470. It was found that solvent ratio and extraction temperature were the main effective factors in this extraction proess. Particularly, the extraction efficiency of ferulic acid, $\gamma$-oryzanol and $\alpha$-toopherol components were significantly affected by extraction temperature. As a result, optimum extraction conditions were 20.35 mL/g of solvent ratio, 79.4$^{\circ}C$ of extraction temperature and 2.88 hr of extraction time. Predicted values at the optimized conditions were acceptable when compared with experimental values.

• Korean Journal of Environmental Agriculture
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• v.37 no.4
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• pp.268-276
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• 2018

BACKGROUND: In the month of January 2018, fine dust alerts and warnings were issued 36 times for $PM_{10}$ and 81 times for PM2.5. Air quality is becoming a serious issue nation-wide. Although interest in air-purifying plants is growing due to the controversy over the risk of chemical substances of regular air-purifying solutions, industrial spread of the plants has been limited due to their efficiency in air-conditioning perspective. METHODS AND RESULTS: This study aims to propose a vegetation-based bio-filter system that can assure total indoor air volume for the efficient application of air-purifying plants. In order to evaluate the quantitative performance of the system, time-series analysis was conducted on air-conditioning performance, indoor air quality, and comfort index improvement effects in a lecture room-style laboratory with 16 persons present in the room. The system provided 4.24 ACH ventilation rate and reduced indoor temperature by $1.6^{\circ}C$ and black bulb temperature by $1.0^{\circ}C$. Relative humidity increased by 24.4% and deteriorated comfort index. However, this seemed to be offset by turbulent flow created from the operation of air blowers. While $PM_{10}$ was reduced by 39.5% to $22.11{\mu}g/m^3$, $CO_2$ increased up to 1,329ppm. It is interpreted that released $CO_2$ could not be processed because light compensation point was not reached. As for the indoor comfort index, PMV was reduced by 83.6 % and PPD was reduced by 47.0% on average, indicating that indoor space in a comfort range could be created by operating vegetation-based bio-filters. CONCLUSION: The study confirmed that the vegetation-based bio-filter system is effective in lowering indoor temperature and $PM_{10}$ and has positive effects on creating comfortable indoor space in terms of PMV and PPD.

• Journal of the Korea Organic Resources Recycling Association
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• v.27 no.2
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• pp.31-40
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• 2019

In Korea, 51,013 thousand tons of livestock manure was generated in 2018. A total of 46,530 thousand tons, which is 91.2% of the total amount of livestock manure generated, was treated by composting(40,647 thousand tons) or liquid fertilization(5,884 thousand tons) method. At present, the policy of livestock manure treatment in Korea is to make livestock manure into organic fertilizer(compost, liquid fertilizer) and then to applicate it on agricultural land. And this policy is very effective in terms of livestock manure treatment and nutrient recycling. However, considering the steadily declining farmland area for decades, the use of livestock manure compost could be limited in the future. There is also concern that local nutrient overloading, nutrient management regulation, and restrictions on the number of livestock may become serious problem for livestock manure treatment. In addition, there are some opinions that nutrient derived from livestock manure may flow into tributaries of major dams. In recent years, there has been a suspicion that fine dust may be generated from livestock manure compost. In recent years, the use of livestock manure fertilizer has been rapidly increasing, there is a growing demand of the development of new technologies for livestock manure treatment. Especially, cow excretes a larger amount of manure than other livestock, so that the efficiency of development of new technology for cow manure treatment will be high. Therefore, in this study, the combustion characteristics of cow manure pellet were investigated in order to analyzed whether cow manure could be used as source of solid fuel. During the combustion test, the weight loss of the cow manure pellet began to increase when the temperature of the combustion chamber reached $300^{\circ}C$. The ratio of $H_2$, $CH_4$, CO in the pyrolysis gas produced in the pyrolysis process of cow manure pellet were 6.65~11.62%, 0.58~1.54 and 11.47~14.07%, respectively.

• Journal of Climate Change Research
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• v.1 no.3
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• pp.189-203
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• 2010

The objective of this study is the estimate of $CO_2$ emissions by the energy consumption of functional technology introduced by classifying energy use in households according to functions as well as energy resources. This study also intends to provide the practical basis data in order to establish specific alternatives for GHG mitigation in residential sector with examining the cause analysis affecting $CO_2$ emission increases from 1995 to 2007. The results of this study show a 6.6% increase in the total $CO_2$ from 60,636 thousand tons in 1995 to 64,611 thousand tons in 2007 by using energy in residential sector. Heating is the greatest $CO_2$ emission sector by use, followed electric appliances, cooking, lighting and cooling. Heating sector shows 56.6% reductions from 71.5% in 1995 and as do cooling and electric home appliances, with a 2.4% increase from 0.6% and a 21.8% increase from 14.2% respectively. To analyze factors resulted in $CO_2$ emissions in residential sector, the relevant indicator change rate from 2005 to 2007 was examined. The results find that population, the number of household, housing areas, family patterns, and family income resulted in the $CO_2$ emissions increase in residential sector from 1995 to 2007. On the other hand, carbon intensity and energy intensity contribute to $CO_2$ reduction in residential sector with -2% and -38.7% respectively because of the energy conversion and the improvement of energy efficiency in electronic appliances. This study can be used as a reference when taken account of the reality and considered the introduction of highly effective measures to increase the possibility of mitigation potential in residential sector hereafter.

• Journal of Wetlands Research
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• v.20 no.4
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• pp.424-431
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• 2018

Stormwater runoff containing considerable amounts of pollutants such as particulates, organics, nutrients, and heavy metals contaminate natural bodies of water. At present, best management practices (BMP) intended to reduce the volume and treat pollutants from stormwater runoff were devised to serve as cost-effective measures of stormwater management. However, improper design and lack of proper maintenance can lead to degradation of the facility, making it unable to perform its intended function. This study evaluated an infiltration trench (IT) that went through a series of maintenance operations. 41 monitored rainfall events from 2009 to 2016 were used to evaluate the pollutant removal capabilities of the IT. Assessment of the water quality and hydrological data revealed that the inflow volume was the most relative factor affecting the unit pollutant loads (UPL) entering the facility. Seasonal variations also affected the pollutant removal capabilities of the IT. During the summer season, the increased rainfall depths and runoff volumes diminished the pollutant removal efficiency (RE) of the facility due to increased volumes that washed off larger pollutant loads and caused the IT to overflow. Moreover, the system also exhibited reduced pollutant RE for the winter season due to frozen media layers and chemical-related mechanisms impacted by the low winter temperature. Maintenance operations also posed considerable effects of the performance of the IT. During the first two years of operation, the IT exhibited a decrease in pollutant RE due to aging and lack of proper maintenance. However, some events also showed reduced pollutant RE succeeding the maintenance as a result of disturbed sediments that were not removed from the geotextile. Ultimately, the presented effects of maintenance operations in relation to the pollutant RE of the system may lead to the optimization of maintenance schedules and procedures for BMP of same structure.

• Phonetics and Speech Sciences
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• v.11 no.3
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• pp.69-76
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• 2019

This study aimed to investigate the effects of vocal aerobic treatment (VAT) on the improvement of voice in patients with voice disorders. Twenty patients (13 males, 7 females) were diagnosed with voice disorders on the basis of videostroboscopy and voice evaluations. Acoustic evaluation was performed with the Multidimensional voice program (MDVP) and Voice Range Profile (VRP) of Computerized Speech Lab (CSL), and aerodynamic evaluation with PAS (Phonatory Aerodynamic System). The changes in F0, Jitter, Shimmer, and NHR before and after treatment were measured by MDVP. F0 range and Energy range were measured with VRP before and after treatment, and the changes in Expiratory Volume (FVC), Phonation Time (PHOT), Mean Expiratory Airflow (MEAF), Mean Peak Air Pressure (MPAP), and Aerodynamic Efficiency (AEFF) with PAS. Videostroboscopy was performed to evaluate the regularity, symmetry, mucosal wave, and amplitude changes of both vocal cords before and after treatment. Voice therapy was performed once a week for each patient using the VAT program in a holistic voice therapy approach. The average number of treatments per patient was 6.5. In the MDVP, Jitter, Shimmer, and NHR showed statistically significant decreases (p < .001, p < .01, p < .05). VRP results showed that Hz and semitones in the frequency range improved significantly after treatment (p < .01, p < .05), as did PAS, FVC, and PHOT (p < .01, p < .001). The results for videostroboscopy, functional voice disorder, laryngopharyngeal reflux, and benign vocal fold lesions were normal. Thus, the VAT program was found to be effective in improving the acoustic and aerodynamic aspects of the voice of patients with voice disorders. In future studies, the effect of VAT on the same group of voice disorders should be studied. It is also necessary to investigate subjective voice improvement and objective voice improvement. Furthermore, it is necessary to examine the effects of VAT in professional voice users.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.