• The Journal of Engineering Geology
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• v.31 no.3
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• pp.433-445
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• 2021

Plasma blasting by high voltage arc discharge were performed in laboratory-scale soil samples to investigate the fluid penetration efficiency. A plasma blasting device with a large-capacity capacitor and columnar soil samples with a diameter of 80 cm and a height of 60 cm were prepared. Columnar soil samples consist of seven A-samples mixed with sand and silt by ratio of 7:3 and three B-samples by ratio of 9:1. When fluid was injected into A-sample by pressure without plasma blasting, fluid penetrated into soil only near around the borehole, and penetration area ratio was less than 5%. Fluid was injected by plasma blasting with three different discharge energies of 1 kJ, 4 kJ and 9 kJ. When plasma blasting was performed once in the A-samples, penetration area ratios of the fluid were 16-25%. Penetration area ratios were 30-48% when blastings were executed five times consecutively. The largest penetration area by plasma blasting was 9.6 times larger than that by fluid injection by pressure. This indicates that the higher discharge energy of plasma blasting and the more numbers of blasting are, the larger are fluid penetration areas. When five consecutive plasma blasting were carried out in B-sample, fluid penetration area ratios were 33-59%. Penetration areas into B-samples were 1.1-1.4 times larger than those in A-samples when test conditions were the same, indicating that the higher permeability of soil is, the larger is fluid penetration area. The fluid penetration radius was calculated to figure out fluid penetration volume. When the fluid was injected by pressure, the penetration radius was 9 cm. Whereas, the penetration radius was 27-30 cm when blasting were performed 5 times with energy of 9 kJ. The radius increased up to 333% by plasma blasting. All these results indicate that cleaning agent penetrates further and remediation efficiency of contaminated soil will be improved if plasma blasting technology is applied to in situ cleaning of contaminated soil with low permeability.

• Journal of Bio-Environment Control
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• v.30 no.4
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• pp.429-440
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• 2021

During the growing period, the integrated solar radiation inside the greenhouse was 12.7MJ·m^-2d^-1, and which was 90% of the average daily global radiation outside the greenhouse, 14.1MJ·m^-2d^-1. The 24-hour average temperature inside the greenhouse from July to August, which has the highest temperature of the year, was 3.04℃ lower than the outside temperature, and 4.07℃ lower after the rainy season. Before the operation of fog cooling system, the average daily RH (%) was lowered to a minimum of 40% (20% for daytime), making it inappropriate for paprika cultivation, but after the operation of fog system, the daily RH during the daytime increased to 70 to 85%. The average humidity deficit increased to a maximum of 12.7g/m³ before fog supply, but decreased to 3.7g/m³ between July and August after fog supply, and increased again after October. The daytime residual CO₂ concentration inside the greenhouse was 707 ppm on average during the whole growing period. The marketable yield of paprika harvested from July 27th to November 23rd, 2020 was higher in 'DSP-7054' and 'Allrounder' with 14,255kg/10a and 14,161kg/10a, respectively, followed by 'K-Gloria orange', 'Volante' and 'Nagono'. There were significant differences between paprika cultivars in fruit length, fruit diameter, soluble solids (°Brix), and flash thickness (mm) of paprika produced in summer season at large single-span plastic greenhouse. The soluble solids content was higher in the orange cultivars 'DSP-7054' and 'Naarangi' and the flesh thickness was higher in the yellow and orange cultivars, with 'K-Gloria orange' and 'Allrounder' being the thickest. The marketable yield of paprika, which was treated with cooling and heating treatments in the root zone, increased by 16.1% in the entire cultivars compared to the untreated ones, increased by 16.5% in 'Nagano', 10.3% in the 'Allrounder', 20.2% in the 'Naarangi', and 17.3% in 'Raon red'.

• Journal of Food Hygiene and Safety
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• v.36 no.5
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• pp.430-439
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• 2021

Essential oils with excellent antibacterial activity were used to study the inhibitory effect against the six types of food poisoning biofilms formed on the surfaces of polyethylene (PE) and stainless steel (SS) that are widely used for food processing instruments and containers. The antibacterial activity of 20 kinds of essential oils was tested using the disk diffusion method. The result showed the degree of antibacterial activity in the following order: cinnamon> clove> lemongrass> peppermint> pine needle (highest to lowest). The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of cinnamon and clove oil were in the range of 0.63-1.25 mg/mL and 1.25-2.50 mg/mL, respectively. The MIC and MBC of lemongrass oil were 1.25-2.50 mg/mL and 2.50-5.00 mg/mL, respectively, showing slightly less antibacterial activity. Although the preventive effect of three types of essential oils on the biofilm formation differed slightly depending on food poisoning bacteria, PE, and SS, it was found that the precoating of 0.5% cinnamon, clove, and lemongrass oil on the PE and SS affects the formation of biofilm. Increased essential oil concentration significantly inhibited the biofilm formation for all food poisoning bacteria (P<0.05), and biofilms of Listeria monocytogenes and Staphylococcus aureus were not formed when treated with 0.5% cinnamon and clove oil. The elimination effect of food poisoning bacteria biofilms formed on the surfaces of PE and SS differed depending on the type of food poisoning bacteria. Still, the biofilm elimination effect increased as the essential oil concentration increased, and the biofilm elimination rate of clove oil was generally high. Therefore, this study found that the cinnamon and clove essential oils (0.5%) are suitable natural materials that effectively prevent, inhibit, and remove the biofilms formed by the food poisoning bacteria on the surfaces of polyethylene and stainless steel.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1281-1293
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• 2021

The recent launch of the GOCI-II enables South Korea to have the world's first capability in deriving the ocean color data at geostationary satellite orbit for about 20 years. It is necessary to develop a consistent long-term ocean color time-series spanning GOCI to GOCI-II mission and improve the accuracy through validation using in situ data. To assess the GOCI-II's early mission performance, the objective of this study is to compare the GOCI-II Chlorophyll-a concentration (Chl-a), Colored Dissolved Organic Matter (CDOM), and remote sensing reflectances (R_rs) through comparison with the GOCI data. Overall, the distribution of GOCI-II Chl-a corresponds with that of the GOCI over the Yellow Sea, Korea Strait, and the Ulleung Basin. In particular, a smaller RMSE value (0.07) between GOCI and GOCI-II over the summer Ulleung Basin confirms the GOCI-II data's reliability. However, despite the excellent correlation, the GOCI-II tends to overestimate Chl-a than the GOCI over the Yellow Sea and Korea Strait. The similar over-estimation bias of the GOCI-II is also notable in CDOM. Whereas no significant bias or error is found for R_rs at 490 nm and 550 nm (RMSE~0), the underestimation of R_rs at 443 nm contributes to the overestimation of GOCI-II Chl-a and CDOM over the Yellow Sea and the Korea Strait. Also, we show over-estimation of GOCI-II R_rs at 660 nm relative to GOCI to cause a possible bias in Total suspended sediment. In conclusion, this study confirms the initial reliability of the GOCI-II ocean color products, and upcoming update of GOCI-II radiometric calibration will lessen the inconsistency between GOCI and GOCI-II ocean color products.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1295-1305
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• 2021

Satellite-derived ocean color products are required to effectively monitor clear open ocean and coastal water regions for various research fields. For this purpose, accurate correction of atmospheric effect is essential. Currently, the Geostationary Ocean Color Imager (GOCI)-II ground segment uses the reanalysis of meteorological fields such as European Centre for Medium-Range Weather Forecasts (ECMWF) or National Centers for Environmental Prediction (NCEP) to correct gas absorption by water vapor and ozone. In this process, uncertainties may occur due to the low spatiotemporal resolution of the meteorological data. In this study, we develop water vapor absorption correction model for the GK-2 combined GOCI-II atmospheric correction using Advanced Meteorological Imager (AMI) total precipitable water (TPW) information through radiative transfer model simulations. Also, we investigate the impact of the developed model on GOCI products. Overall, the errors with and without water vapor absorption correction in the top-of-atmosphere (TOA) reflectance at 620 nm and 680 nm are only 1.3% and 0.27%, indicating that there is no significant effect by the water vapor absorption model. However, the GK-2A combined water vapor absorption model has the large impacts at the 709 nm channel, as revealing error of 6 to 15% depending on the solar zenith angle and the TPW. We also found more significant impacts of the GK-2 combined water vapor absorption model on Rayleigh-corrected reflectance at all GOCI-II spectral bands. The errors generated from the TOA reflectance is greatly amplified, showing a large error of 1.46~4.98, 7.53~19.53, 0.25~0.64, 14.74~40.5, 8.2~18.56, 5.7~11.9% for from 620 nm to 865 nm, repectively, depending on the SZA. This study emphasizes the water vapor correction model can affect the accuracy and stability of ocean color products, and implies that the accuracy of GOCI-II ocean color products can be improved through fusion with GK-2A/AMI.

• Journal of Family Resource Management and Policy Review
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• v.25 no.1
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• pp.15-34
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• 2021

Purpose : This study aims to identify the influence of individual, parental, workplace, and policy variables on the intention of marriage of unmarried workers. The results of the study can be used as a basis to come up with policy and practical measures to increase these workers' intention of marriage. Methods : Data was collected from 480 participants through a questionnaire. The measuring tools in this study were tested for reliability and validity. In addition, technical statistics, ANOVA, correlation analysis, and hierarchical regression analysis were performed by SPSS 18.0. Results : First, unmarried workers' willingness to marry was higher above the average. Second, significant differences in the unmarried workers' intention of marriage were revealed based on gender, age, level of education, total assets, and debt status. Significant associations were also found with parents' economic levels and the possibility of parental support for housing and marriage expenses. The differences in response based on employment patterns, job satisfaction, and family-friendly organizational culture were also significant. The correlation between policy variables and marriage intentions also showed the necessity of a funded housing policy and a marriage preparation policy. Third, based on the hierarchical regression analysis using personal, parental, and workplace variables to identify the factors affecting unmarried workers' willingness to marry, it is clear that gender, age, and the likelihood of parental support for housing and marriage expenses have a significant impact. A family-friendly organizational culture and funded housing policy were also significant contributors. Conclusion : The study found that the funded housing policy was the most important factor affecting unmarried office workers' willingness to marry. Additionally, the possibility of parental support for housing and marriage expenses and a family-friendly organizational culture at work were found to have an important impact on the improvement of unmarried workers' willingness to marry.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.33-53
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• 2021

In the crossroads of social change caused by the spread of the Fourth Industrial Revolution and the prolonged COVID-19, the Korean government announced the Digital New Deal policy on July 14, 2020. The Digital New Deal policy's primary goal is to create new businesses by accelerating digital transformation in the public sector and industries around data, networks, and artificial intelligence technologies. However, in a rapidly changing social environment, information asymmetry of the future benefits of technology can cause differences in the public's ability to analyze the direction and effectiveness of policies, resulting in uncertainty about the practical effects of policies. On the other hand, the media leads the formation of discourse through communicators' role to disseminate government policies to the public and provides knowledge about specific issues through the news. In other words, as the media coverage of a particular policy increases, the issue concentration increases, which also affects public decision-making. Therefore, the purpose of this study is to verify the dynamic relationship between the media coverage and the stock market on the Korean government's digital New Deal policy using Granger causality, impulse response functions, and variance decomposition analysis. To this end, the daily stock turnover ratio, daily price-earnings ratio, and EWMA volatility of digital technology-based companies related to the digital new deal policy among KOSDAQ listed companies were set as variables. As a result, keyword search volume, daily stock turnover ratio, EWMA volatility have a bi-directional Granger causal relationship with media coverage. And an increase in media coverage has a high impact on keyword search volume on digital new deal policies. Also, the impulse response analysis on media coverage showed a sharp drop in EWMA volatility. The influence gradually increased over time and played a role in mitigating stock market volatility. Based on this study's findings, the amount of media coverage of digital new deals policy has a significant dynamic relationship with the stock market.

• Korean Journal of Environment and Ecology
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• v.35 no.5
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• pp.525-547
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• 2021

The main purpose of this study is to examine the use of plant species in ecological restoration projects. To this end, planting drawings from 58 sites that had completed the return of the ecosystem conservation cooperation fund for the past six years were collected and analyzed. The analysis used the construction completion and design drawings to determine the overall selection status and analyze frequency by classifying planted plants into wild and cultivated plants by nature, size, vegetation climate, and upland and wetland habitat. The investigation and analysis process found many cases of wrong plant names, so an analysis was also performed on the matter. In the 58 investigation sites, 282 plants were used for planting: 91 tree species, 69 shrub species, 11 vine species, and 111 herbal species. The most commonly used plant species was Spiraea prunifolia f. simpliciflora, followed by Sorbus alnifolia, Quercus acutissima, Zoysia japonica, Callicarpa dichotoma, and Weigela subsessilisin that order. The most commonly used tree species was Sorbus alnifolia,followed by Quercus acutissima, Zelkova serrata, Chionanthus retusus, and Cornus officinalis, in that order. The most commonly used shrub species was Spiraea prunifolia f. simpliciflora, followed by Weigela subsessilis, Callicarpa dichotoma, Rhododendron yedoense f. poukhanense. and Euonymus alatusin that order. The most commonly used herbal plant species was Zoysia japonica, followed by Dendranthema zawadskii var. latilobum, Aster koraiensis, Miscanthus sacchariflorus, and Pennisetum alopecuroidesin that order. In the analysis by vegetation climate, Spiraea prunifolia f. simpliciflora, Callicarpa dichotoma, and Sorbus alnifoliawere most used in that order in both the temperate central and the warm temperate forest zones, but the pattern does not properly reflect the climate characteristics. In the analysis by habitat, Miscanthus sacchariflorus and Lythrum salicariawere most used in the wetland. In particular, the ratio of wild plants to cultivated plants was 76% to 24%, indicating the ratio of selecting cultivated plants was high. The names of plants on the drawings were mostly common names that did not appear in the Korea National Arboretum or the National Species List of Korea. It is necessary to use proper plant names in the future. Regarding the use of planting plants for ecological restoration, it is necessary to adopt the approach of diversifying selected plants, selecting plants according to characteristics of climate zones, and lowering the specifications of plants used for ecological restoration. Moreover, it is important to fully understand the ecological characteristics of wetland plants and minimize the ratio of using cultivated plants to ensure the plant selection centered on wild plants.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.795-805
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• 2021

In this study, after developing an LSTM-based deep learning model for estimating daily runoff in the Soyang River Dam basin, the accuracy of the model for various combinations of model structure and input data was investigated. A model was built based on the database consisting of average daily precipitation, average daily temperature, average daily wind speed (input up to here), and daily average flow rate (output) during the first 12 years (1997.1.1-2008.12.31). The Nash-Sutcliffe Model Efficiency Coefficient (NSE) and RMSE were examined for validation using the flow discharge data of the later 12 years (2009.1.1-2020.12.31). The combination that showed the highest accuracy was the case in which all possible input data (12 years of daily precipitation, weather temperature, wind speed) were used on the LSTM model structure with 64 hidden units. The NSE and RMSE of the verification period were 0.862 and 76.8 m³/s, respectively. When the number of hidden units of LSTM exceeds 500, the performance degradation of the model due to overfitting begins to appear, and when the number of hidden units exceeds 1000, the overfitting problem becomes prominent. A model with very high performance (NSE=0.8~0.84) could be obtained when only 12 years of daily precipitation was used for model training. A model with reasonably high performance (NSE=0.63-0.85) when only one year of input data was used for model training. In particular, an accurate model (NSE=0.85) could be obtained if the one year of training data contains a wide magnitude of flow events such as extreme flow and droughts as well as normal events. If the training data includes both the normal and extreme flow rates, input data that is longer than 5 years did not significantly improve the model performance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.5
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• pp.1-14
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• 2021

Innovation and Scale-up of Start-up companies are becoming important national tasks. In the past, it was spread the start-up policy paradigm such as 'Start-up America', 'Start-up Chile', 'Start-up Britain' to overcome the recession globally. However as the economic recovery has become more visible recently in advanced economies, it is shifting from a start-up support policy to a scale-up oriented policy paradigm such as 'Scale-up America', Scale-up UK', 'Scale-up Denmark'. It is necessary to enter the scale-up phase beyond the start-up phase to increase the number of high-quality jobs and to continue economic growth. Therefore, it is necessary to grow the start-up into a strong medium-sized company and to lay the foundation for survival. Therefore, the purpose of this study is to consider the antecedent factors that influence the scale-up aspiration for the start-up firm to grow into a scale-up company, and empirically identifies the differences between the stages of economic development and entrepreneurs in the country. In order to accomplish the purpose, this study predicted scale-up by aspiration which is a predictor of scale-up behavior because it is difficult to achieve visible growth in a short period of time due to the characteristics of start-up companies. In order to empirically explore these relationships, the data were collected from nascent entrepreneurs who have less than 3.5 years of the Adult Population Survey(APS) among the subjects surveyed by the Global Entrepreneurship Monitor(GEM) and the national economic development stage are divided into Innovation-driven, Efficiency-driven, Factor-driven type economies. For the test hypotheses, this study adopted the multi-level model analysis for comparison between national economic development stages and using the R 3.5.0 program. The results of this study are as follows. There is difference between the national economic development and the entrepreneur in the relationship between innovation orientation of entrepreneurs and scale-up aspirations. As the economy of the country develops, the innovation activity of the entrepreneur becomes more active. Since start-ups are heavily influenced by entrepreneurs, there is a difference in the degree of aspiration depending on how innovative an entrepreneur is in the same environment. In terms of the relationship between innovation orientation and scale-up aspiration, the fear of failure was found to differ between national economic development and entrepreneurs. The fear of failure differ from country to country, and this is one of the important factors affecting entrepreneurial activities. It is expected that the factors influencing the growth of the start-up companies which are identified through the results of these studies, will be used to create a suitable scale-up ecosystem according to the national economic development stage.