• Journal of Food Hygiene and Safety
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• v.38 no.6
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• pp.430-441
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• 2023

Velvet antler is widely used as a traditional medicine, and numerous studies have demonstrated its tremendous nutritional and medicinal values including immunity-enhancing effects. This study aimed to investigate different deer velvet extracts (Sample 1: raw extract, Sample 2: dried extract, and Sample 3: freeze-dried extract) for proximate composition, uronic acid, sulfated glycosaminoglycan, sialic acid, collagen levels, and chemical components using ultra-performance liquid chromatography-quadrupole-time-of-light mass spectrometry. In addition, we evaluated the cytotoxic effect of the deer velvet extracts on BV2 microglia, HT22 hippocampal cells, HaCaT keratinocytes, and RAW264.7 macrophages using the cell viability MTT assay. Furthermore, we evaluated acute toxicity of the deer velvet extracts at different doses (0, 500, 1000, and 2000 mg/kg) administered orally to both male and female ICR mice for 14 d (five mice per group). After treatment, we evaluated general toxicity, survival rate, body weight changes, mortality, clinical signs, and necropsy findings in the experimental mice based on OECD guidelines. The results suggested that in vitro treatment with the evaluated extracts had no cytotoxic effect in HaCaT keratinocytes cells, whereas Sample-2 had a cytotoxic effect at 500 and 1000 ㎍/mL on HT22 hippocampal cells and RAW264.7 macrophages. Sample 3 was also cytotoxic at concentrations of 500 and 1000 ㎍/mL to RAW264.7 and BV2 microglial cells. However, the mice treated in vivo with the velvet extracts at doses of 500-2000 mg/kg BW showed no clinical signs, mortality, or necropsy findings, indicating that the LD50 is higher than this dosage. These findings indicate that there were no toxicological abnormalities connected with the deer velvet extract treatment in mice. However, further human and animal studies are needed before sufficient safety information is available to justify its use in humans.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.915-929
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• 2023

Improvements in people's quality of life, diversification of leisure activities, and changes in population structure have led to an increase in the demand for tourism and an expansion of the diversification of tourism activities. In particular, for coastal cities where land and marine tourism elements coexist, various factors influence their tourism demands. Tourism requires the construction of infrastructure and content development according to the demand at the tourist destination. This study aims to improve the prediction accuracy and explore influencing factors through time series analysis of tourism scale using agent-based data. Basic local governments in the Busan area were examined, and the data used were the number of tourists and the amount of tourism consumption on a monthly basis. The univariate time series analysis, which is a deterministic model, was used along with the SARIMAX analysis to identify the influencing factor. The tourism consumption propensity, focusing on the consumption amount according to business types and the amount of mentions on SNS, was set as the influencing factor. The difference in accuracy (RMSE standard) between the time series models that did and did not consider COVID-19 was found to be very wide, ranging from 1.8 times to 32.7 times by region. Additionally, considering the influencing factor, the tourism consumption business type and SNS trends were found to significantly impact the number of tourists and the amount of tourism consumption. Therefore, to predict future demand, external influences as well as the tourists' consumption tendencies and interests in terms of local tourism must be considered. This study aimed to predict future tourism demand in a coastal city such as Busan and identify factors affecting tourism scale, thereby contributing to policy decision-making to prepare tourism demand in consideration of government tourism policies and tourism trends.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.426-441
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• 2023

This study investigated the predictive accuracy of a model of landslide displacement in Jecheon-si, where a great number of landslides were triggered by heavy rain on both natural (non-clear-cut) and clear-cut slopes during August 2020. This was accomplished by applying three flow direction methods (single flow direction, SFD; multiple flow direction, MFD; infinite flow direction, IFD) and the degree of root cohesion to an infinite slope stability equation. The application assumed that the soil saturation and any changes in root cohesion occurred following the timber harvest (clear-cutting). In the study area, 830 landslide locations were identified via landslide inventory mapping from satellite images and 25 cm resolution aerial photographs. The results of the landslide modeling comparison showed the accuracy of the models that considered changes in the root cohesion following clear-cutting to be improved by 1.3% to 2.6% when compared with those not considered in the area under the receiver operating characteristics (AUROC) analysis. Furthermore, the accuracy of the models that used the MFD algorithm improved by up to 1.3% when compared with the models that used the other algorithms in the AUROC analysis. These results suggest that the discriminatory application of the root cohesion, which considers changes in the vegetation condition, and the selection of the flow direction method may influence the accuracy of landslide predictive modeling. In the future, the results of this study should be verified by examining the root cohesion and its dynamic changes according to the tree species using the field hydrological monitoring technique.

• Journal of Korean Society of Forest Science
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• v.112 no.4
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• pp.561-573
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• 2023

National forests have an advantage over private forests in terms of higher investment in capital, technology, and labor, allowing for more intensive management. As such, national forests are expected to serve not only as a strategic reserve of forest resources to address the long-term demand for timber but also to stably perform various essential forest functions demanded by society. However, most forest stands in the current national forests belong to the fourth age class or above, indicating an imminent timber harvesting period amid an imbalanced age class structure. Therefore, if timber harvesting is not conducted based on systematic management planning, it will become difficult to ensure the continuity of the national forests' diverse functions. This study was conducted to determine the optimal volume of timber production in the national forests to improve the age-class structure while sustainably maintaining their economic and public functions. To achieve this, the study first identified areas within the national forests suitable for timber production. Subsequently, a forest management planning model was developed using multi-objective linear programming, taking into account both the national forests' economic role and their public benefits. The findings suggest that approximately 488,000 hectares within the national forests are suitable for timber production. By focusing on management of these areas, it is possible to not only improve the age-class distribution but also to sustainably uphold the forests' public benefits. Furthermore, the potential volume of timber production from the national forests for the next 100 years would be around 2 million m³ per year, constituting about 44% of the annual domestic timber supply.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1211-1224
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• 2023

Clouds cause many difficult problems in observing land surface phenomena using optical satellites, such as national land observation, disaster response, and change detection. In addition, the presence of clouds affects not only the image processing stage but also the final data quality, so it is necessary to identify and remove them. Therefore, in this study, we developed a new cloud detection technique that automatically performs a series of processes to search and extract the pixels closest to the spectral pattern of clouds in satellite images, select the optimal threshold, and produce a cloud mask based on the threshold. The cloud detection technique largely consists of three steps. In the first step, the process of converting the Digital Number (DN) unit image into top-of-atmosphere reflectance units was performed. In the second step, preprocessing such as Hue-Value-Saturation (HSV) transformation, triangle thresholding, and maximum likelihood classification was applied using the top of the atmosphere reflectance image, and the threshold for generating the initial cloud mask was determined for each image. In the third post-processing step, the noise included in the initial cloud mask created was removed and the cloud boundaries and interior were improved. As experimental data for cloud detection, CAS500-1 L2G images acquired in the Korean Peninsula from April to November, which show the diversity of spatial and seasonal distribution of clouds, were used. To verify the performance of the proposed method, the results generated by a simple thresholding method were compared. As a result of the experiment, compared to the existing method, the proposed method was able to detect clouds more accurately by considering the radiometric characteristics of each image through the preprocessing process. In addition, the results showed that the influence of bright objects (panel roofs, concrete roads, sand, etc.) other than cloud objects was minimized. The proposed method showed more than 30% improved results(F1-score) compared to the existing method but showed limitations in certain images containing snow.

• Journal of Venture Innovation
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• v.5 no.1
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• pp.59-73
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• 2022

Untact marketing is being activated due to COVID-19. As a result, live commerce, an untact seller, is also active in the e-commerce market. Therefore, in this study, we tried to find out what factors influence consumers when they purchase through live commerce. In particular, since consumers' trust and flow in live commerce platforms and products is important, their mediating effects were analyzed. The research model was established by deriving common variables among the characteristics of live commerce based on previous studies. An online survey was conducted for empirical analysis. 200 users who made at least one purchase in live commerce were analyzed. The study results are as follows. Among the characteristics of live commerce, entertainment, economics, professionality were found to have a positive (+) effect on purchase intention. On the other hand, ease of use did not significantly affect purchase intention. The influence was shown in the order of entertainment, professionality and economics. The mediating effect of trust was found to play a mediating role in that entertainment, economics, and professionality affect purchase intention. On the other hand, a significant mediating effect was not tested between ease of use and purchase intention. As for the mediating effect of flow, it was found that flow plays a mediating role in that entertainment and economics affect purchase intention. On the other hand, the mediating effect of flow in terms of ease of use and economics affecting purchase intention was not tested. As for the multiple mediating effect of flow and trust, the mediating effect of flow was stronger than the mediating effect of trust when entertainment had an effect on purchase intention. In terms of professionality affecting purchase intention, the mediating effect of flow was also stronger than the mediating effect of trust. On the other hand, it was analyzed that only trust had a mediating effect when economics had an effect on purchase intention. The results of this study empirically tested that entertainment, which is a fun and interesting factor of live commerce content, is the most important factor when consumers use live commerce. In addition, various results were derived, such as cases where trust and flow act as mediators at the same time or not at all. Practical implications can be found in that it provided a clue about what to prioritize in order to reach consumers for live commerce platform.

• Journal of Venture Innovation
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• v.4 no.3
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• pp.125-139
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• 2021

As the 4th industrial revolution progresses, new technologies and services are being born, growing, and maturing. Now, beyond the mobile era, the metaverse is being discussed as a new paradigm. Therefore, in this study, in preparation for the metaverse era, we tried to analyze what factors have an important influence when consumers want to use new technologies. In particular, the research was conducted focusing on how the context in which consumers use the technology changes depending on whether they are B2C or B2B. For this, augmented reality (AR) was selected in the B2C context by linking the research subject with the metaverse era, and the smart factory was selected in the B2B context. The research model for the analysis was established by deriving and setting common influence variables by reflecting the characteristics of the research target technology based on the modified extended unified theory of acceptance and use of technology. A survey was conducted for empirical analysis, and 150 AR and 150 smart factory subjects were analyzed. The empirical study results are as follows. The relationship between performance expectancy and intention to use, technology readiness and intention to use was found to have a significant positive (+) effect on both AR and smart factory. On the other hand, it was found that effort expectancy, social influence, and trust had a positive (+) effect on intention to use only in AR. Only in smart factory, facilitating conditions had a significant positive (+) effect on intention to use. It was also found that the perceived risk had a significant negative (-) effect on the intention to use only in the smart factory. The results of this study are academically significant in that we empirically test that influencing factors of technology use varies depending on the context in which it is used by consumers. In practice, it provided an implication of what to focus on first is being implemented.

• Journal of Venture Innovation
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• v.5 no.2
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• pp.49-66
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• 2022

This study intends to examine how the perception of covid-19 risk affects consumers' consumption behavior based on previous studies in a situation where the spread of covid-19 is prolonged. This study demonstrates how consumers' perception of covid-19 risk affects online and offline consumption behavior through the perceived severity, perceived vulnerability, coping effectiveness, and self-efficacy of the revised protective motivation theory (Rogers, 1983). We want to test it through analysis. In order to achieve the purpose of this study, consumers living in Seoul and Gyeonggi Province who have purchased within the past 3 months were selected as a sample. In addition, variable data such as risk perception of covid-19, perceived severity, perceived vulnerability, coping effectiveness, self-efficacy, online purchase attitude and purchase intention, offline purchase attitude and purchase intention were collected through the questionnaire.A total of 363 copies of valid responses were tested to test the hypothesis of the relationship between variables through the covariance structure model. The analysis results of this study were first, that covid-19 risk perception had a significant positive (+) effect on perceived severity, perceived vulnerability, and coping effectiveness. Second, perceived severity and perceived vulnerability were found to have a significant positive (+) effect on offline purchasing attitude. Third, perceived severity, perceived vulnerability, coping plan effectiveness, and self-efficacy were all found to have significant positive (+) effects on online purchase attitude. Finally, it was found that offline purchase attitude and online purchase attitude had a significant positive (+) effect on offline purchase intention and online purchase intention, respectively. Also, it was found that online purchase attitude had a negative (-) effect on offline purchase intention. The results of this analysis will provide meaningful implications for the establishment of strategies for distribution channels according to the social risk of infectious diseases.

• Journal of Venture Innovation
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• v.4 no.2
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• pp.57-75
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• 2021

KHNP's shared growth activities are based on such public good. Reflecting the characteristics of a comprehensive energy company, a high-tech plant company, and a leading company for shared growth, it presents strategies to link performance indicators with its partners and implements various measures. Key tasks include maintaining the nuclear power plant ecosystem, improving management conditions for partner companies, strengthening future capabilities of the nuclear power plant industry, and supporting a virtuous cycle of regional development. This is made by reflecting the specificity of nuclear power generation as much as possible, and is designed to reflect the spirit of shared growth through win-win and cooperation in order to solve the challenges of the times while considering the characteristics as much as possible as possible. KHNP's shared growth activities can be said to be the practice of the spirit of the times(Zeitgeist). The spirit of the times given to us now is that companies should strive for sustainable growth as social air. KHNP has been striving to establish a creative and leading shared growth ecosystem. In particular, considering the positions of partners, it has been promoting continuous system improvement to establish a fair trade culture and deregulation. In addition, it has continuously discovered and implemented new customized support projects that are effective for partner companies and local communities. To this end, efforts have been made for shared growth through organic collaboration with partners and stakeholders. As detailed tasks, it also presents fostering new markets and new industries, maintaining supply chains, and emergency support for COVID-19 to maintain the nuclear power plant ecosystem. This reflects the social public good after the recent COVID-19 incident. In order to improve the management conditions of partner companies, productivity improvement, human resources enhancement, and customized funding are being implemented as detailed tasks. This is a plan to practice win-win growth with partner companies emphasized by corporate social responsibility (CSR) and ISO 26000 while being faithful to the main job. Until now, ESG management has focused on the environmental field to cope with the catastrophe of climate change. According to KHNP is presenting a public enterprise-type model in the environmental field. In order to strengthen the future capabilities of the nuclear power plant industry as a state-of-the-art energy company, it has set tasks to attract investment from partner companies, localization and new technologies R&D, and commercialization of innovative technologies. This is an effort to develop advanced nuclear power plant technology as a concrete practical measure of eco-friendly development. Meanwhile, the EU is preparing a social taxonomy to focus on the social sector, another important axis in ESG management, following the Green Taxonomy, a classification system in the environmental sector. KHNP includes enhancing local vitality, increasing income for the underprivileged, and overcoming the COVID-19 crisis as part of its shared growth activities, which is a representative social taxonomy field. The draft social taxonomy being promoted by the EU was announced in July, and the contents promoted by KHNP are consistent with this, leading the practice of social taxonomy