• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.1-20
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• 2023

The present study has monitored the changes in the biodiversity of Dunchon-Dong ecological and landscape conservation area after the restoration of the wetland, identified and analyzed the threats to the ecosystem, and presented a management plan accordingly. In this area which was forests and rice paddies in the past, apartment reconstruction is currently underway, with some hinterland forests and wetlands remaining. When we look into the change in the floras, the total number of species was 193 in 2000 before the restoration, it decreased from 2004 to 2006, and as of 2019, it was 149, showing an increasing trend. The result of comparing the species that emerged before and after the restoration showed an increase in Cyperaceae herbs such as Carex maximowiczii and Carex dispalata growing in wetland areas within forests and Schoenoplectiella juncoides and Schoenoplectus tabernaemontani growing in areas within wetlands where shallow water is maintained. As a result of analyzing the change in the area ratio of each type of extant vegetation, the wetland native herbs formed the power at the highest ratio after the restoration. The change in the power of the wetland native herbs was on an increasing trend until 2007, after which it decreased much in 2010 and then gradually increased, showing values of 26.6% in 2000, 44.6% in 2002, 49.0% in 2005, 53.3% in 2007, 28.7% in 2010, and 37.3% in 2019. The cause of the decrease in 2010 was judged to be due to the vegetation management conducted to secure open water and remove organic matter in freshwater reservoirs. The amphibia which emerged from 2000 to 2019 was a total of 9 species including Hynobius leeshii, Bufo gargarizans, Kaloula borealis, and Rana uenoi. As a result of the changes in the emerging wild birds, the species diversity index before the restoration was 0.9922 in 2000, and the species diversity index after the restoration gradually increased to 1.2449 in 2005, 1.2467 in 2010, and 2.2631 in 2019. The amphibia and wild birds inhibiting in the Dunchon-Dong forest and wetland were judged to have increased through continuous wetland maintenance such as naturalized plant removal management, native plant maintenance, and open water securing management. For the ecosystem preservation management of the Dunchon-Dong ecological and landscape conservation area, it was suggested to minimize the impact of the Dunchon-Dong reconstruction project, reorganize the indiscriminate access roads adjacent to the wetland, and reorganize the main entrance to the wetland. For ecosystem restoration management, systematic restoration and ecological buffer planting were suggested to be carried out at the time of construction fence demolition.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1615-1633
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• 2023

Sargassum horneri is one of the floating algae in the sea, which breeds in large quantities in the Yellow Sea and East China Sea and then flows into the coast of Republic of Korea, causing various problems such as destroying the environment and damaging fish farms. In order to effectively prevent damage and preserve the coastal environment, the development of Sargassum horneri detection algorithms using satellite-based remote sensing technology has been actively developed. However, incorrect detection information causes an increase in the moving distance of ships collecting Sargassum horneri and confusion in the response of related local governments or institutions,so it is very important to minimize false detections when producing Sargassum horneri spatial information. This study applied technology to automatically remove false detection results using the GOCI-II-based Sargassum horneri detection algorithm of the National Ocean Satellite Center (NOSC) of the Korea Hydrographic and Oceanography Agency (KHOA). Based on the results of analyzing the causes of major false detection results, it includes a process of removing linear and sporadic false detections and green algae that occurs in large quantities along the coast of China in spring and summer by considering them as false detections. The technology to automatically remove false detection was applied to the dates when Sargassum horneri occurred from February 24 to June 25, 2022. Visual assessment results were generated using mid-resolution satellite images, qualitative and quantitative evaluations were performed. Linear false detection results were completely removed, and most of the sporadic and green algae false detection results that affected the distribution were removed. Even after the automatic false detection removal process, it was possible to confirm the distribution area of Sargassum horneri compared to the visual assessment results, and the accuracy and precision calculated using the binary classification model averaged 97.73% and 95.4%, respectively. Recall value was very low at 29.03%, which is presumed to be due to the effect of Sargassum horneri movement due to the observation time discrepancy between GOCI-II and mid-resolution satellite images, differences in spatial resolution, location deviation by orthocorrection, and cloud masking. The results of this study's removal of false detections of Sargassum horneri can determine the spatial distribution status in near real-time, but there are limitations in accurately estimating biomass. Therefore, continuous research on upgrading the Sargassum horneri monitoring system must be conducted to use it as data for establishing future Sargassum horneri response plans.

• Journal of Korea Water Resources Association
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• v.56 no.12
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• pp.993-1003
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• 2023

Due to the increasing greenhouse gas emissions, the global mean temperature has risen by 1.1℃ compared to pre-industrial levels, and significant changes are expected in functioning of water supply systems. In this study, we assessed impacts of climate change and instreamflow management on water supply reliability in the Geum River basin, Korea. We proposed univariate climate response functions, where mean precipitation and potential evaporation were coupled as an explanatory variable, to assess impacts of climate stress on multiple water supply reliabilities. To this end, natural streamflows were generated in the 19 sub-basins with the conceptual GR6J model. Then, the simulated streamflows were input into the Water Evaluation And Planning (WEAP) model. The dynamic optimization by WEAP allowed us to assess water supply reliability against the 2020 water demand projections. Results showed that when minimizing the water shortage of the entire river basin under the 1991-2020 climate, water supply reliability was lowest in the Bocheongcheon among the sub-basins. In a scenario where the priority of instreamflow maintenance is adjusted to be the same as municipal and industrial water use, water supply reliability in the Bocheongcheon, Chogang, and Nonsancheon sub-basins significantly decreased. The stress tests with 325 sets of climate perturbations showed that water supply reliability in the three sub-basins considerably decreased under all the climate stresses, while the sub-basins connected to large infrastructures did not change significantly. When using the 2021-2050 climate projections with the stress test results, water supply reliability in the Geum River basin was expected to generally improve, but if the priority of instreamflow maintenance is increased, water shortage is expected to worsen in geographically isolated sub-basins. Here, we suggest that the climate response function can be established by a single explanatory variable to assess climate change impacts of many sub-basin's performance simultaneously.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.21-35
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• 2023

Expectations surrounding generative AI technology and its profound ramifications are sweeping across various industrial domains. Given the anticipated pivotal role of the startup ecosystem in the utilization and advancement of generative AI technology, it is imperative to cultivate a deeper comprehension of the present state and distinctive attributes characterizing venture capital (VC) investments within this domain. The current investigation delves into South Korea's landscape of VC investment deals and prognosticates the projected VC investments by juxtaposing these against the United States, the frontrunner in the generative AI industry and its associated ecosystem. For analytical purposes, a compilation of 286 investment deals originating from 117 U.S. generative AI startups spanning the period from 2008 to 2023, as well as 144 investment deals from 42 South Korean generative AI startups covering the years 2011 to 2023, was amassed to construct new datasets. The outcomes of this endeavor reveal an upward trajectory in the count of VC investment deals within both the U.S. and South Korea during recent years. Predominantly, these deals have been concentrated within the early-stage investment realm. Noteworthy disparities between the two nations have also come to light. Specifically, in the U.S., in contrast to South Korea, the quantum of recent VC deals has escalated, marking an augmentation ranging from 285% to 488% in the corresponding developmental stage. While the interval between disparate investment stages demonstrated a slight elongation in South Korea relative to the U.S., this discrepancy did not achieve statistical significance. Furthermore, the proportion of VC investments channeled into generative AI enterprises, relative to the aggregate number of deals, exhibited a higher quotient in South Korea compared to the U.S. Upon a comprehensive sectoral breakdown of generative AI, it was discerned that within the U.S., 59.2% of total deals were concentrated in the text and model sectors, whereas in South Korea, 61.9% of deals centered around the video, image, and chat sectors. Through forecasting, the anticipated VC investments in South Korea from 2023 to 2029 were derived via four distinct models, culminating in an estimated average requirement of 3.4 trillion Korean won (ranging from at least 2.408 trillion won to a maximum of 5.919 trillion won). This research bears pragmatic significance as it methodically dissects VC investments within the generative AI domain across both the U.S. and South Korea, culminating in the presentation of an estimated VC investment projection for the latter. Furthermore, its academic significance lies in laying the groundwork for prospective scholarly inquiries by dissecting the current landscape of generative AI VC investments, a sphere that has hitherto remained void of rigorous academic investigation supported by empirical data. Additionally, the study introduces two innovative methodologies for the prediction of VC investment sums. Upon broader integration, application, and refinement of these methodologies within diverse academic explorations, they stand poised to enhance the prognosticative capacity pertaining to VC investment costs.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.49-65
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• 2022

This study conducted an exploratory study based on in-depth interviews to understand the characteristics and capabilities of female entrepreneurs to promote women entrepreneurship. Therefore, in this study, through in-depth interviews with eight female entrepreneurs, the main contents of entrepreneurial attitudes (need for independence, development desire, favorable conditions), start-up entry rate, start-up motivation, start-up activities and constraints were analyzed. As a result, first, it was found that the entrepreneurial attitude of female entrepreneurs has a strong motivation for successful management based on a feeling of self-satisfaction, has characteristics that prioritize independence and self-actualization, and favorable conditions for starting a business are important. Second, it was found that women's individual differences from men and social structural factors had no significant effect on the entry rate of women. Third, it was found that the most important entrepreneurship motivation for women is the spirit of challenge, self-satisfaction, and the desire to balance work and family. Fourth, female entrepreneurs showed little difference in perception between male and female entrepreneurs in terms of resource access, but there was some discrimination in the network. Fifth, the main industries of female entrepreneurs are small businesses, and there is a tendency to be concentrated in industries with low profit margins and low growth and sales. Finally, it was found that barriers to women's entrepreneurship still exist. Based on the results of this study, the following implications are suggested. First, this study is differentiated in that it mainly identified the characteristics of women's experiences and social environments while starting a business and running a business. Second, in the case of female entrepreneurs, there is a need to spread a positive awareness of women entrepreneurship by arguing that the barriers to entrepreneurship unique to women are not high and can be sufficiently overcome. Lastly, although opportunistic start-ups based on women's social experience or management ability in work life are important for women's entrepreneurship, government support policies are needed to promote professional technology start-ups.

• Health Policy and Management
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• v.34 no.1
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• pp.78-86
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• 2024

Background: The purpose of this study was to determine the effectiveness of the specialty hospital system by comparing the medical use of inpatients who had artificial joint replacement surgery in specialty hospitals and non-specialty hospitals. Methods: This study utilized 2021-2022 healthcare benefit claims data provided by the Health Insurance Review and Assessment Service. The dependent variable is inpatient medical use which is measured in terms of charges per case and length of stay. The independent variable was whether the hospital was designated as a specialty hospital, and the control variables were patient-level variables (age, gender, insurer type, surgery type, and Charlson comorbidity index) and medical institution-level variables (establishment type, classification, location, number of orthopedic surgeons, and number of nurses). Results: The results of the multiple regression analysis between charges per case and whether a hospital is designated as a specialty hospital showed a statistically significant negative relationship between charges per case and whether a hospital is designated as a specialty hospital. This suggests a significant low in charges per case when a hospital is designated as a specialty hospital compared to a non-specialty hospital, indicating that there is a difference in medical use outcomes between specialty hospitals and non-specialty hospitals inpatients. Conclusion: The practical implications of this study are as follows. First, the criteria for designating specialty hospitals should be alleviated. In our study, the results show that specialty hospitals have significantly lower per-case costs than non-specialty hospitals. Despite the cost-effectiveness of specialty hospitals, the high barriers to be designated for specialty hospitals have gathered the specialty hospitals in metropolitan and major cities. To address the regional imbalance of specialty hospitals, it is believed that ease the criteria for designating specialty hospitals in non-metropolitan areas, such as introducing "semi-specialty hospitals (tentative name)," will lead to a reduction in health disparities between regions and reduce medical costs. Second, it is necessary to determine the appropriateness of the size of hospitals' medical staff. The study found that the number of orthopedic surgeons and nurses varied in charges per case. Therefore, it is believed that appropriately allocating hospital medical staff can maximize the cost-effectiveness of medical services and ultimately reduce medical costs.

• Journal of Wetlands Research
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• v.26 no.1
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• pp.21-31
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• 2024

Macromia daimoji Okumura, 1949 was designated as an endangered species and also categorized as Class II Endangered wildlife on the International Union for Conservation of Nature (IUCN) Red List in Korea. The spatial distribution of this species ranged within a region delimited by northern latitude from Sacheon-si(35.1°) to Yeoncheon-gun(38.0°) and eastern longitude from Yeoncheon-gun(126.8°) to Yangsan-si(128.9°). They generally prefer microhabitats such as slowly flowing littoral zones of streams, alluvial stream islands and temporarily formed puddles in the sand-based lowland streams. The objectives of this study were to analyze the similarity of benthic macroinvertebrate communities in M. daimoji habitats, to predict the current potential distribution patterns as well as the changes of distribution ranges under global climate change circumstances. Data was collected both from the Global Biodiversity Information Facility (GBIF) and by field surveys from April 2009 to September 2022. We adopted MaxEnt model to predict the current and future potential distribution for M. daimoji using downloaded 19 variables from the WorldClim database. The differences of benthic macroinvertebrate assemblages in the mainstream of Nakdonggang were smaller than those in its tributaries and the other streams, based on the surrounding environments and stream sizes. MaxEnt model presented that potential distribution displayed high inhabiting probability in Nakdonggang and its tributaries. Applying to the future scenarios by Intergovernmental Panel on Climate Change (IPCC), SSP1 scenario was predicted to expand in a wide area and SSP5 scenario in a narrow area, comparing with current potential distribution. M. daimoji is not only directly threatened by physical disturbances (e.g. river development activities) but also vulnerable to rapidly changing climate circumstances. Therefore, it is necessary to monitor the habitat environments and establish conservation strategies for preserving population of M. daimoji.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.173-189
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• 2023

As supreme technologies continue to be developed, industries such as artificial intelligence, biotechnology, robots, aerospace, electric vehicles, and solar energy are created, and the macro business environment is rapidly changing. Due to these large-scale changes and increased complexity, it is necessary to pay attention to the effect of social capital, which can create new value by utilizing capital increasing the importance of relationships rather than technology or asset ownership itself at the level of start-up strategy. Social capital is a concept first proposed by Hanifan in 1916, and refers to the overall sum of capabilities or resources that are latent or available for use in mutual, continuous, organic relationships or accumulated human relationship networks between individuals or social members. In addition, the diversity of start-up teams with diverse backgrounds, characteristics, and capabilities, rather than one exceptional founder, has been emphasized. Founding team diversity refers to the diversity of in-depth factors such as demographic factors, beliefs, and values of the founding team. In addition, changes in the macro environment are emphasizing the importance of technology start-ups and laboratory start-ups that lead industrial innovation and create the nation's core growth engines. This study focused on the I-Corps' program. I-Corps, which means innovation corps, is a laboratory startup program launched by the National Research Foundation (NSF) in 2011 to encourage entrepreneurship and commercialization of research results. It focuses on forming a startup team involving professors, researchers and market discovery activities. Taking these characteristics into account, this study empirically verified the impact of social capital from a network perspective and founding team diversity on I-Corps start-up performance. As a result of the analysis, the educational diversity of the founding team had a negative (-) effect on the financial performance of the founding team. On the other side, the gender diversity and the cognitive dimension of social capital had a positive (+) effect on the financial performance of the founding team. This study is expected to provide more useful theoretical and practical implications regarding the diversity, social capital, and performance interpretation of the I-Corps Lab startup team.

• Journal of Bio-Environment Control
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• v.32 no.4
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• pp.384-395
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• 2023

Since transpiration plays a key role in optimal irrigation management, knowledge of the irrigation demand of crops like tomatoes, which are highly susceptible to water stress, is necessary. One way to determine irrigation demand is to measure transpiration, which is affected by environmental factor or growth stage. This study aimed to estimate the transpiration amount of tomatoes and find a suitable model using mathematical and deep learning models using minute-by-minute data. Pearson correlation revealed that observed environmental variables significantly correlate with crop transpiration. Inside air temperature and outside radiation positively correlated with transpiration, while humidity showed a negative correlation. Multiple Linear Regression (MLR), Polynomial Regression model, Artificial Neural Network (ANN), Long short-term Memory (LSTM), and Gated Recurrent Unit (GRU) models were built and compared their accuracies. All models showed potential in estimating transpiration with R² values ranging from 0.770 to 0.948 and RMSE of 0.495 mm/min to 1.038 mm/min in the test dataset. Deep learning models outperformed the mathematical models; the GRU demonstrated the best performance in the test data with 0.948 R² and 0.495 mm/min RMSE. The LSTM and ANN closely followed with R² values of 0.946 and 0.944, respectively, and RMSE of 0.504 m/min and 0.511, respectively. The GRU model exhibited superior performance in short-term forecasts while LSTM for long-term but requires verification using a large dataset. Compared to the FAO56 Penman-Monteith (PM) equation, PM has a lower RMSE of 0.598 mm/min than MLR and Polynomial models degrees 2 and 3 but performed least among all models in capturing variability in transpiration. Therefore, this study recommended GRU and LSTM models for short-term estimation of tomato transpiration in greenhouses.