• Composites Research
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• v.36 no.1
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• pp.1-15
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• 2023

Demand for energy consumption reduction is increasing according to the development expectations of future mobility. Lightweight structural materials are known as a method to reduce greenhouse gas emissions and improve energy efficiency. In particular, fiber reinforced polymer composite (FRP) is attracting attention as a material that can replace existing metal alloys due to its excellent mechanical properties and light weight. In this paper, industrial applications and research trends of carbon fiber reinforced composites (CFRP, carbon FRP) and self-reinforced composites (SRC) were reviewed based on the reinforcement, polymer matrix, and manufacturing process. In order to overcome the expensive process cost and long manufacturing time of the epoxy resin-based autoclave method, which is mainly used in the aircraft field, mass production of CFRP-applied electric vehicles has been reported using a high-pressure resin transfer molding process including fast-curing epoxy. In addition, thermoplastic resin-based CFRP and interface enhancement methods to solve the recycling issue of carbon fiber composites were reviewed in terms of materials and processes. To form a perfect matrix-reinforcement interface, which is known as the major factor inducing the excellent mechanical properties of FRP, studies on SRC impregnated with the same matrix in polymer fibers have been reported. The physical and mechanical properties of SRC based on various thermoplastic polymers were reviewed in terms of polymer orientation and composite structure. In addition, a copolymer matrix strategy for extending the processing window of highly drawn polypropylene fiber-based SRC was discussed. The application of CFRP and SRC as lightweight structural materials can provide potential options for improving the energy efficiency of future mobility.

• Journal of The Korean Association For Science Education
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• v.43 no.1
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• pp.1-15
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• 2023

As the social and economic value of living modified organisms (LMOs) increase, so do the potential risks they pose to humans and the environment. Therefore, all laboratories using LMOs must establish an LMO laboratory in accordance with the standards required by regulations. Recently, in high school, LMO-related experimental programs have been developed for their educational effects. Also, in this case, it is necessary to comply with the regulation for LMO laboratories. However, high schools are still unfamiliar with the LMO laboratory, and it is difficult for teachers to manage an LMO laboratory because its implementation applies the same standards to general research institutes. In this study, we used causal chain analysis to discover the difficulties each teacher faced while setting up an LMO laboratory by examining three cases. The difficulties experienced by teachers are as follows: the first problem is "reluctance to set up an LMO laboratory," because of "administrative tasks for laboratory registration" and "difficulty in persuading colleagues." The second problem is a difficulty for teachers to operate LMO laboratory in blind spots, due to "inflexible installation and closure," "medical waste disposal," and "LMO education that does not fit the school context." Through this study, although the difficulty of running an LMO laboratory is caused by a lack of necessity and insufficient consideration of the school context, the more fundamental cause was a lack of collaborative planning between the educational field and the operating institutions. The teachers who participate in this research suggest that "using shared LAB" and "preparing opportunities for knowledge sharing" can be considered as strategies for operating the school's LMO laboratory. We feel that this study will provide a useful reference for teachers or schools planning to build an LMO laboratory.

• Journal of Wetlands Research
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• v.25 no.2
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• pp.145-158
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• 2023

Constructed wetlands (CWs) are effective technologies for urban wastewater management, utilizing natural physico-chemical and biological processes to remove pollutants. This study employed a bibliometric analysis approach to investigate the progress and future research trends in the field of CWs. A comprehensive review of 100 most-recently published and open-access articles was performed to analyze the performance of CWs in treating wastewater. Spain, China, Italy, and the United States were among the most productive countries in terms of the number of published papers. The most frequently used keywords in publications include water quality (n=19), phytoremediation (n=13), stormwater (n=11), and phosphorus (n=11), suggesting that the efficiency of CWs in improving water quality and removal of nutrients were widely investigated. Among the different types of CWs reviewed, hybrid CWs exhibited the highest removal efficiencies for BOD (88.67%) and TSS (95.67%), whereas VSSF, and HSSF systems also showed high TSS removal efficiencies (83.25%, and 78.83% respectively). VSSF wetland displayed the highest COD removal efficiency (71.82%). Generally, physical processes (e.g., sedimentation, filtration, adsorption) and biological mechanisms (i.e., biodegradation) contributed to the high removal efficiency of TSS, BOD, and COD in CW systems. The hybrid CW system demonstrated highest TN removal efficiency (60.78%) by integrating multiple treatment processes, including aerobic and anaerobic conditions, various vegetation types, and different media configurations, which enhanced microbial activity and allowed for comprehensive nitrogen compound removal. The FWS system showed the highest TP removal efficiency (54.50%) due to combined process of settling sediment-bound phosphorus and plant uptake. Phragmites, Cyperus, Iris, and Typha were commonly used in CWs due to their superior phytoremediation capabilities. The study emphasized the potential of CWs as sustainable alternatives for wastewater management, particularly in urban areas.

• Korean Journal of Remote Sensing
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• v.39 no.3
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• pp.325-338
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• 2023

Korean fir (Abies koreana Wilson) is one of the most important environmental indicator tree species for assessing climate change impacts on coniferous forests in the Korean Peninsula. However, due to the nature of alpine and subalpine regions, it is difficult to conduct regular field surveys of Korean fir, which is mainly distributed in regions with altitudes greater than 1,000 m. Therefore, this study analyzed the vegetation change trend of Korean fir using regularly observed remote sensing data. Specifically, normalized difference vegetation index (NDVI) from Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature (LST), and precipitation data from Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievalsfor GPM from September 2003 to 2020 for Hallasan and Jirisan were used to analyze vegetation changes and their association with environmental variables. We identified a decrease in NDVI in 2020 compared to 2003 for both sites. Based on the NDVI difference maps, areas for healthy vegetation and high mortality of Korean fir were selected. Long-term NDVI time-series analysis demonstrated that both Hallasan and Jirisan had a decrease in NDVI at the high mortality areas (Hallasan: -0.46, Jirisan: -0.43). Furthermore, when analyzing the long-term fluctuations of Korean fir vegetation through the Hodrick-Prescott filter-applied NDVI, LST, and precipitation, the NDVI difference between the Korean fir healthy vegetation and high mortality sitesincreased with the increasing LST and decreasing precipitation in Hallasan. Thissuggests that the increase in LST and the decrease in precipitation contribute to the decline of Korean fir in Hallasan. In contrast, Jirisan confirmed a long-term trend of declining NDVI in the areas of Korean fir mortality but did not find a significant correlation between the changes in NDVI and environmental variables (LST and precipitation). Further analyses of environmental factors, such as soil moisture, insolation, and wind that have been identified to be related to Korean fir habitats in previous studies should be conducted. This study demonstrated the feasibility of using satellite data for long-term monitoring of Korean fir ecosystems and investigating their changes in conjunction with environmental conditions. Thisstudy provided the potential forsatellite-based monitoring to improve our understanding of the ecology of Korean fir.

• Korean Journal of School Psychology
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• v.15 no.3
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• pp.459-488
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• 2018

The self-report assessment has been most commonly used to estimate bullying/victimization (B/V) rates in most domestic and international prevalence studies. However, the presence of many potential biasing factors in such an assessment method, including specific operationalization/measurement strategies and testing conditions, has become an issue due to a considerable variability in reported involvement rates across studies. This study analyzed self-reported B/V involvement rates on Olweus Bullying Questionnaire (OBQ) among 690 Korean middle school students by gender and two different cut-offs (generous vs. strict cut-offs) and examined if the involvement rates were significantly varied by testing conditions such as presentation vs. omission of a precise definition of B/V, anonymous vs. non-anonymous/confidential administration, and the use of global vs. specific questions. Chi-square analyses revealed that boys displayed higher involvement rates on global measures of B/V and on items related to direct forms of B/V, with no significant gender differences on specific measures of relational B/V rates. It was also found that a global rate of bullying and specific rates of verbal B/V were 111% to 157% higher when no definition was provided. However, anonymous vs. non-anonymous administration had no significant impacts on rates of involvement, except for one item; there were also no significant differences in reported degrees of frankness and perceived confidentiality of their responses across two adminstration conditions. Finally, when involvement rates were assessed by using specific vs. global items, they were 68% to 148% higher with binominal correlations in low to moderate ranges. Findings also indicated that global items had a high specificity but a relatively low sensitivity. Implications of these findings were fully discussed for researchers and practitioners in the field of B/V assessment.

• Economic and Environmental Geology
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• v.56 no.6
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• pp.729-744
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• 2023

The value of lithium has significantly increased due to the rising demand for electric cars and batteries. Lithium is primarily found in pegmatites, hydrothermally altered tuffaceous clays, and continental brines. Globally, groundwater-fed salt lakes and oil field brines are attracting attention as major sources of lithium in continental brines, accounting for about 70% of global lithium production. Recently, deep groundwater, especially geothermal water, is also studied for a potential source of lithium. Lithium concentrations in deep groundwater can increase through substantial water-rock reaction and mixing with brines. For the exploration of lithim in deep groundwater, it is important to understand its origin and behavior. Therefore, based on a nationwide preliminary study on the hydrogeochemical characteristics and evolution of thermal groundwater in South Korea, this study aims to investigate the distribution of lithium in the deep groundwater environment and understand the geochemical factors that affect its concentration. A total of 555 thermal groundwater samples were classified into five hydrochemical types showing distinct hydrogeochemical evolution. To investigate the enrichment mechanism, samples (n = 56) with lithium concentrations exceeding the 90th percentile (0.94 mg/L) were studied in detail. Lithium concentrations varied depending upon the type, with Na(Ca)-Cl type being the highest, followed by Ca(Na)-SO₄ type and low-pH Ca(Na)-HCO₃ type. In the Ca(Na)-Cl type, lithium enrichment is due to reverse cation exchange due to seawater intrusion. The enrichment of dissolved lithium in the Ca(Na)-SO₄ type groundwater occurring in Cretaceous volcanic sedimentary basins is related to the occurrence of hydrothermally altered clay minerals and volcanic activities, while enriched lithium in the low-pH Ca(Na)-HCO₃ type groundwater is due to enhanced weathering of basement rocks by ascending deep CO₂. This reconnaissance geochemical study provides valuable insights into hydrogeochemical evolution and economic lithium exploration in deep geologic environments.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.107-124
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• 2023

As getting in 4th Industrial Revolution Times, Continuing Education Enrolled Students(CEES) trying to find loophole for jepordized current life and need job transfer have surged their interest significantly on starting new business to bring up their post career after retirement through self-improvement. Government and university have actively initiated diverse policies of promoting startup for CEES in kicking off entrepreneurship courses and programs. However, relevant main policy, 'The 2nd University Startup Education Five-Year Plan (draft)' have too chiefly focused on theoretical start-up education rather than practical courses, causing the problem of inappropriate support for implementing real startup and business (Ministry of Education, 2018). This study is brought to empirically investigate the effect of self-efficacy as perspective of the impact of entrepreneurship education on entrepreneurial intention and behavior to come up with problem of poor entrepreneurial environment and entrepreneurship education to CEES. As to empirical research, this paper deliver on-line survey to CEES from September to October 2022, collect 207 effective feedbacks, In order to verify the reliability of the scale, the Cronbach's Alpha Coefficient (Cronbach's α) was calculated, analyzed, and measured. For hypothesis test, this paper utilize the multiple regression analysis statistical analysis method and use the SPSS 22.0 statistical processing program. Empirical results show, first, it was found that self-efficacy had a significant effect on start-up education. Second, start-up education had a significant effect on the intention to start a business of adult learners. Third, start-up education had a significant effect on the start-up behavior of adult learners. Fourth, self-efficacy had a significant effect on the intention of adult learners to start a business. Fifth, self-efficacy had a significant effect on the start-up behavior of adult learners. Sixth, self-efficacy had a mediating effect in the relationship between entrepreneurship education and adult learners' intention to start a business. Seventh, self-efficacy had a complete mediating effect in the relationship between start-up education and adult learners' start-up behavior. This paper is brought three significant implications. First, main consideration developing entrepreneurship education tools for CEES need to falls on defining potential needs of CEES as segmenting as to coming up with diversity of CEES's characteristics such as gender, age, experience, education, and occupation. Second, as to design specific entrepreneurship education program, both practical training program of utilizing CEES's career field experience benchmarking best practice startup and venture cases from domestic and global, and professional startup program of CEES initiating directly startup from ideation to develop business plan with pitching and discussing. Third, entrepreneurship education for CEES should be designed to incubate self-efficacy to enhance entrepreneurial intention of implementing entrepreneurial behavior as a real, eventually leading solid support system of self-improvement for CEES' Retirement life planning.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.1-15
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• 2024

This study aims to elucidate the characteristics of big tech platform companies' sports content business in an environment of rapid digital transformation. Specifically, this study examines the market structure of big tech platform companies with a focus on Amazon, revealing the role of sports content within this structure through an analysis of Amazon's sports marketing business and provides an outlook on the sports content business of big tech platform companies. Based on two-sided market platform business models, big tech platform companies incorporate sports content as a strategy to enhance the value of their platforms. Therefore, sports content is used as a tool to enhance the value of their platforms and to consolidate their monopoly position by maximizing profits by increasing the synergy of platform ecosystems such as infrastructure. Amazon acquires popular live sports broadcasting rights on a continental or national basis and supplies them to its platforms, which not only increases the number of new customers and purchasing effects, but also provides IT solution services to sports organizations and teams while planning and supplying various promotional contents, thus creates synergy across Amazon's platforms including its advertising business. Amazon also expands its business opportunities and increases its overall value by supplying live sports contents to Amazon Prime Video and Amazon Prime, providing technical services to various stakeholders through Amazon Web Services, and offering Amazon Marketing Cloud services for analyzing and predicting advertisers' advertising and marketing performance. This gives rise to a new paradigm in the sports marketing business in the digital era, stemming from the difference in market structure between big tech companies based on two-sided market platforms and legacy global companies based on one-sided markets. The core of this new model is a business through the development of various contents based on live sports streaming rights, and sports content marketing will become a major field of sports marketing along with traditional broadcasting rights and sponsorship. Big tech platform global companies such as Amazon, Apple, and Google have the potential to become new global sports marketing companies, and the current sports marketing and advertising companies, as well as teams and leagues, are facing both crises and opportunities.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.