• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Korean Journal of Environment and Ecology
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• v.36 no.5
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• pp.481-495
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• 2022

This study was designed to identify the actual state of naturalized plants and invasive alien species that cause disturbances to the ecosystem, plants which are introduced after forest restoration, and explore the implications resulting from the project. Onsite examination included 29 sites which have been subjected to forest restoration by the Korea Forest Service. Once these were chosen, activity took place twice a year in the spring (May-June) and in the summer (August-September) in 2020 and 2021. Areas not relevant to the project sites were excluded from this activity so that we could identify the plants that could be understood to have been introduced or brought into the site after the actual forest restoration. And the correlation was analyzed, between the naturalized flora within the project sites and the working types applied to the site through confirmation of completion of the restoration project. The naturalized plants appearing on the entire site cover a total of 109 taxa, which includes 29 families, 80 genera, 108 species and 1 subspecies, while invasive plants included 3 families, 7 genera and 8 species. The number of classifications and the naturalization rate gradually decreased over time, after the project. While there was no significant difference between the number of classification groups and the naturalization rate for naturalized plants between project sites, given the number of taxa of naturalized plants, organized by type of damage, there were relatively more naturalized plants that appeared in the severed section of the Baekdudaegan Mountain Range, as well as at quarry and facility sites. Seeding apparently results in naturalization rates as high as 15.545%, on average, based on comparisons of naturalization rates by sowing, seeding, planting, herb planting, and sod pitching channels, all of these being methods of vegetation for planting/greening of bareland and slopes within the project areas. With no seeding, it was 9.167%, higher than the average. As for other vegetation, there was no significant difference depending on application of the working type. This means that unlike the plants subjected to planting, the working type of seed planting which makes it difficult to identify whether a certain plant is a naturalized plant greatly affects the introduction of naturalized plants to the restoration sites, even when using herb planting and sod pitching to control plants and results. Therefore the study suggests that there be inspection by experts of seeds when sowing within restoration sites. The results of this study suggest good practices that will help to direct effective vegetation restoration and follow-up management.

• Journal of Wetlands Research
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• v.25 no.3
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• pp.184-195
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• 2023

To develop an effective management plan for Ambrosia trifida L., an invasive alien plant in Korea, we assessed the potential of Ophraella communa LeSage as a biological control agent. This involved investigating the host specificity of the herbivore Ophraella communa LeSage, its annual travel distance, and the impact of this insect on the fitness of Ambrosia trifida L. We confirmed the host plant preference of Ophraella communa LeSage. The travel distance of this insect was determined by monitoring its appearance in selected Ambrosia trifida L. communities without these insects at distances of 10, 20, 30, and 100 meters, based on the locations where the presence of Ophraella communa LeSage was observed. The growth, reproductive, and physiological responses of Ambrosia trifida L. were measured according to feeding by Ophraella communa LeSage. As a result, Ophraella communa LeSage fed on only three taxa and moved short distances within a radius of 30 m per year from the host. The feeding behavior of the herbivore had a negative impact on the growth, reproductive, and physiological responses of Ambrosia trifida L. And the plant's growth and reproduction improved with increasing distance from the herbivore. Furthermore, the introduction of herbivores was able to control over 90% of Ambrosia trifida L. when the coverage of the Ambrosia trifida L. group was below 50%. However, the effectiveness of the removal decreased when the coverage exceeded 90%. These results are likely to be utilized by Ophraella communa LeSage as an ecological control agent. It is advantageous to introduce them in spring (May) when the coverage is low to maximize the effectiveness of control.

• Journal of Environmental Impact Assessment
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• v.32 no.2
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• pp.94-111
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• 2023

In order to establish an effective management strategy for invasive species early detection and regular monitoring are required to assess their introduction or dispersal. Environmental DNA (eDNA) is actively applied to evaluate the fauna including the presence of invasive species as it has high detection sensitivity and can detect multiple species simultaneously. In Korea, the applicability evaluation of metabarcoding is being conducted mainly on fish, and research on other taxa is insufficient. Therefore, this study identified the feasibility of detecting invasive species in Korea using eDNA metabarcoding. In addition, to confirm the possibility of detection by taxa, the detection of target species was evaluated using four universal primers (MiFish, MiMammal, Mibird, Amp16S) designed for fish, mammals, birds, and amphibians. As a result, target species (Trachemys scripta, 3 sites; Cervus nippon, 3 sites; Micropterus salmoides, 7 sites; Rana catesbeiana, 4 sites) were detected in 17 of the total 55 sites. Even in the selection of dense sampling sites within the study area, there was a difference in the detection result by reflecting the ecological characteristics of the target species. A comparison of community structures (species richness, abundance and diversity) based on the presence of invasive species focused on M.salmoides and T.scripta, showed higher diversity at the point where invasive species were detected. Also, 1 to 4 more species were detected and abundance was also up to 1.7 times higher. The results of invasive species detection through metabarcoding and the comparison of community structures indicate that the accumulation of large amounts of monitoring data through eDNA can be efficiently utilized for multidimensional ecosystem evaluation. In addition, it suggested that eDNA can be used as major data for evaluation and prediction, such as tracking biological changes caused by artificial and natural factors and environmental impact assessment.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.655-667
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• 2023

Algal bloom outbreaks are frequently reported around the world, and serious water pollution problems arise every year in Korea. It is necessary to protect the aquatic ecosystem through continuous management and rapid response. Many studies using satellite images are being conducted to estimate the concentration of chlorophyll-a (Chl-a), an indicator of algal bloom occurrence. However, machine learning models have recently been used because it is difficult to accurately calculate Chl-a due to the spectral characteristics and atmospheric correction errors that change depending on the water system. It is necessary to consider the factors affecting algal bloom as well as the satellite spectral index. Therefore, this study constructed a dataset by considering water quality, hydrological and meteorological factors, and sentinel-2 images in combination. Representative ensemble models random forest and extreme gradient boosting (XGBoost) were used to predict the concentration of Chl-a in eight weirs located on the Nakdong river over the past five years. R-squared score (R²), root mean square errors (RMSE), and mean absolute errors (MAE) were used as model evaluation indicators, and it was confirmed that R² of XGBoost was 0.80, RMSE was 6.612, and MAE was 4.457. Shapley additive expansion analysis showed that water quality factors, suspended solids, biochemical oxygen demand, dissolved oxygen, and the band ratio using red edge bands were of high importance in both models. Various input data were confirmed to help improve model performance, and it seems that it can be applied to domestic and international algal bloom detection.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.191-209
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• 2023

Amid rapid changes in technological innovation due to the influence of the 4th Industrial Revolution and COVID-19, research related to absorption capacity and patent activities to promote technological innovation of Korean technology start-ups is important in this uncertain environment. This study aims to examine the effects on entrepreneurial performance and patent activities by reconstructing absorptive capacity, an organizational capability, for technology-based startups in fields such as BT and ICT with less than seven years of establishment, distinguishing between potential absorptive capacity and realized absorptive capacity. The study also seeks to develop a theoretical research model. To accomplish this, data was collected from managerial executives, including CEOs of 215 technology startups. The following hypotheses were tested: Firstly, potential absorptive capacity had a significant impact on patent activities, while realized absorptive capacity did not. Secondly, potential absorptive capacity had a significant impact on technological performance, while realized absorptive capacity did not. Thirdly, both potential and realized absorptive capacity had a significant impact on financial and non-financial performance. Fourthly, patent activities indirectly influenced potential absorptive capacity and technological performance, but did not affect realized absorptive capacity. Fifthly, patent activities indirectly influenced potential absorptive capacity and financial performance, but did not affect realized absorptive capacity. Lastly, patent activities indirectly influenced potential absorptive capacity and non-financial performance, but did not affect realized absorptive capacity. The practical significance of this study lies in providing useful guidelines for building the core capabilities of organizations through absorptive capacity and patent activities. Furthermore, it is expected that startups that have not recognized the formation process of absorptive capacity for patent activities will perceive the formation mechanism of absorptive capability anew and show considerable interest in future potential and realized absorptive capacity as part of their management strategies. This is anticipated to play an important role in adapting to rapidly changing technological advancements, the startup ecosystem, and securing sustainable competitive advantages.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.197-214
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• 2024

This study aimed to investigate students' perceptions at science and technology specialized universities towards entrepreneurship support policies and to derive policy improvement measures by applying a bottom-up approach to reflect the requirements of the policy beneficiaries, i.e., the students. Specifically, the research explored effective execution strategies for student entrepreneurship support policies through a survey and analysis of KAIST students. The findings revealed that KAIST students recognize the urgent need for improvement in sharing policy objectives with the student entrepreneurship field, reflecting the opinions of the campus entrepreneurship scene in policy formulation, and constructing an entrepreneurship-friendly academic system for nurturing student entrepreneurs. Additionally, there was a highlighted need for enhancement in the capacity of implementing agencies, as well as in marketing and market development capabilities, and organizational management and practical skills as entrepreneurs within the educational curriculum. Consequently, this study proposes the following improvement measures: First, it calls for enhanced transparency and accessibility of entrepreneurship support policies, ensuring students clearly understand policy objectives and can easily access information. Second, it advocates for student-centered policy development, where students' opinions are actively incorporated to devise customized policies that consider their needs and the actual entrepreneurship environment. Third, there is a demand for improving entrepreneurship-friendly academic systems, encouraging more active participation in entrepreneurship activities by adopting or refining academic policies that recognize entrepreneurship activities as credits or expand entrepreneurship-related courses. Based on these results, it is expected that this research will provide valuable foundational data to actively support student entrepreneurship in science and technology specialized universities, foster an entrepreneurial spirit, and contribute to the creation of an innovation-driven entrepreneurship ecosystem that contributes to technological innovation and social value creation.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• Journal of Wetlands Research
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• v.25 no.1
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• pp.35-47
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• 2023

Korean facility horticulture and hydroponic cultivation methods increase, requiring the management of waste water generated. In this study, the amount of fertilizer contained in the discharged waste liquid was determined. By evaluating this as a price, it was suggested to reduce water treatment costs and recycle fertilizer components. It was evaluated based on the results of major water quality analysis of waste liquid by crop, such as tomatoes, paprika, cucumbers, and strawberries, and in the case of P component, it was analyzed by converting it to the amount of phosphoric acid (P₂O₅). The amount of nitrogen (N) can be calculated by discharging 1,145.90kg·ha^-1 of tomatoes, 920.43kg·ha^-1 of paprika, 804.16kg·ha^-1 of cucumbers, 405.83kg·ha^-1 of strawberries, and the fertilizer content of P₂O₅ is 830.65kg·ha^-1 of paprika, 622.32kg·ha^-1 of tomatoes, 477.67kg·ha^-1 of cucumbers. In addition, trace elements such as potassium (K), calcium (Ca), magnesium (Mg), iron (Fe), and manganese (Mn) were also analyzed to be emitted. The price per kg of each item calculated by averaging the price of fertilizer sold on the market can be evaluated as KRW, N 860.7, P 2,378.2, K 2,121.7, Ca 981.2, Mg 1,036.3, Fe 126,076.9, Mn 62,322.1, Zn 15,825.0, Cu 31,362.0, B 4,238.0, Mo 149,041.7. The annual fertilizer loss amount for each crop was calculated by comprehensively considering the price per kg calculated based on the market price of fertilizer, the concentration of waste by crop analyzed earlier, and the average annual emission of hydroponic cultivation. As a result of the analysis, the average of the four hydroponic crops was 5,475,361.1 won in fertilizer ingredients, with tomatoes valued at 6,995,622.3 won, paprika valued at 7,384,923.8 won, cucumbers valued at 5,091,607.9 won, and strawberries valued at 2,429,290.6 won. It was expected that if hydroponic drainage is managed through self-treatment or threshing before discharge rather than by leaking it into a river and treating it as a pollutant, it can be a valuable reusable fertilizer ingredient along with reducing water treatment costs.