• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.523-541
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• 2023

As accessibility to 3D printers increases, there is a growing frequency of exposure to chemicals associated with 3D printing. However, research on the toxicity and harmfulness of chemicals generated by 3D printing is insufficient, and the performance of toxicity prediction using in silico techniques is limited due to missing molecular structure data. In this study, quantitative structure-activity relationship (QSAR) model based on data-centric AI approach was developed to predict the toxicity of new 3D printing materials by imputing missing values in molecular descriptors. First, MissForest algorithm was utilized to impute missing values in molecular descriptors of hazardous 3D printing materials. Then, based on four different machine learning models (decision tree, random forest, XGBoost, SVM), a machine learning (ML)-based QSAR model was developed to predict the bioconcentration factor (Log BCF), octanol-air partition coefficient (Log Koa), and partition coefficient (Log P). Furthermore, the reliability of the data-centric QSAR model was validated through the Tree-SHAP (SHapley Additive exPlanations) method, which is one of explainable artificial intelligence (XAI) techniques. The proposed imputation method based on the MissForest enlarged approximately 2.5 times more molecular structure data compared to the existing data. Based on the imputed dataset of molecular descriptor, the developed data-centric QSAR model achieved approximately 73%, 76% and 92% of prediction performance for Log BCF, Log Koa, and Log P, respectively. Lastly, Tree-SHAP analysis demonstrated that the data-centric-based QSAR model achieved high prediction performance for toxicity information by identifying key molecular descriptors highly correlated with toxicity indices. Therefore, the proposed QSAR model based on the data-centric XAI approach can be extended to predict the toxicity of potential pollutants in emerging printing chemicals, chemical process, semiconductor or display process.

• Journal of Life Science
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• v.33 no.12
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• pp.1002-1014
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• 2023

The root of Cirsium japonicum var. maackii (Maxim.) has long been used in traditional medicine to prevent the onset and progression of various diseases and has been reported to exert a wide range of physiological effects, including antioxidant activity. However, research on its effects on hepatocytes remains scarce. This study used the human hepatocellular carcinoma HepG2 cell line to investigate the antioxidant activity of ethanol extract of C. japonicum root (EECJ) on hepatocytes. Hydrogen peroxide (H₂O₂) was used to mimic oxidative stress. The results showed that EECJ significantly reverted the decrease in cell viability and suppressed the release of lactate dehydrogenase in HepG2 cells treated with H₂O₂. Moreover, an analysis of changes in cell morphology, flow cytometry, and microtubule-associated protein light chain 3 (LC3) expression showed that EECJ significantly inhibited HepG2 cell autophagy induced by H₂O₂. Furthermore, it attenuated H₂O₂-induced apoptosis and cell cycle disruption by blocking intracellular reactive oxygen species and mitochondrial superoxide production, indicating strong antioxidant activity. EECJ also restored the decreased levels of intracellular glutathione (GSH) and enhanced the expression and activity of superoxide dismutase and GSH peroxidase in H₂O₂-treated HepG2 cells. Although an analysis of the components contained in EECJ and in vivo validation using animal models are needed, these findings indicate that EECJ is a promising candidate for the prevention and treatment of oxidative stress-induced liver cell damage.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.1
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• pp.85-106
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• 2023

This study reviewed and derived the success factors of overseas agricultural startups and studied their integrated research model. Agricultural startups and general startups have in common that poor resources and infrastructure exist from a resource-based perspective after startup, but a differentiated approach from general startups is required due to the nature of the primary industry of agriculture. In this study, we approach the company internal factors (human resources/vision/distribution network capacity/capital capacity/cultivated crops/physical resources/farming technology, etc.) and external factors (agricultural infrastructure/laws/regulations/relationship with surrounding society, etc.) We tried to build a research model that can be integrated by focusing on various existing research models, success factors, and entrepreneurship. Through this, it is intended to present an integrated model that is practically helpful to business performance to entrepreneurs, business-related persons, and researchers who need an integrated understanding of agricultural startups at home and abroad. made for purpose In this paper, a standard model was established through three types (existing agricultural startup, small and medium-sized business startup, multinational company, and comprehensive approach) according to size and characteristics for modeling agricultural startup success factors. Through this, a total of 9 success factors (agricultural management, external environment, manager/founder characteristics, corporate identity, business management, organizational culture, infrastructure, commercialization capability, and sustainable growth) were derived. The implication of this study is that the success factors of agricultural startups were comprehensively presented based on 'entrepreneurship' for various domestic and foreign agricultural startup cases. By confirming the systematic categorization, a standard model for future agricultural startup success factors was presented, and as a result, a foundation was presented for systematic research and practical effectiveness of related research in the future.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• 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.18 no.4
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• pp.65-76
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• 2023

In the field of technology entrepreneurship and startups, the development of Artificial Intelligence(AI) has emerged as a key topic for business model innovation. As a result, venture firms are making various efforts centered on AI to secure competitiveness(Kim & Geum, 2023). The purpose of this study is to analyze the relationship between the development of GenAI technology and the startup ecosystem by analyzing domestic news articles to identify trends in the technology startup field. Using BIG Kinds, this study examined the changes in GenAI-related news articles, major issues, and trends in Korean news articles from 1990 to August 10, 2023, focusing on the emergence of ChatGPT before and after, and visualized the relevance through network analysis and keyword visualization. The results of the study showed that the mention of GenAI gradually increased in the articles from 2017 to 2023. In particular, OpenAI's ChatGPT service based on GPT-3.5 was highlighted as a major issue, indicating the popularization of language model-based GenAI technologies such as OpenAI's DALL-E, Google's MusicLM, and VoyagerX's Vrew. This proves the usefulness of GenAI in various fields, and since the launch of ChatGPT, Korean companies have been actively developing Korean language models. Startups such as Ritten Technologies are also utilizing GenAI to expand their scope in the technology startup field. This study confirms the connection between GenAI technology and startup entrepreneurship activities, which suggests that it can support the construction of innovative business strategies, and is expected to continue to shape the development of GenAI technology and the growth of the startup ecosystem. Further research is needed to explore international trends, the utilization of various analysis methods, and the possibility of applying GenAI in the real world. These efforts are expected to contribute to the development of GenAI technology and the growth of the startup ecosystem.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.131-148
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• 2023

The purpose of this study is to investigate the effects of entrepreneurial environment and entrepreneurial competency on entrepreneurial intention by setting degree of self-determination as a moderating variable for pre-entrepreneur of agriculture industry. The entrepreneurial environment was divided into perceived support and perceived barriers, and the sub-variables of entrepreneurial competence were set as creativity, problem solving, communication, marketing, and business plan. 253 questionnaires were used for empirical analysis. The results of the analysis using SPSS v25.0 and Process macro v4.2 are as follows. First, the perceived support and perceived barriers of the entrepreneurial environment have a significant effect on entrepreneurial intention. Creativity, problem solving, marketing and business plan of entrepreneurial competency have a significant effect on entrepreneurial intention, but the effect of communication was non-significant. Second, the degree of self-determination did not moderate the relationship between perceived support, barriers and entrepreneurial intention. This means that the level of self-determination may not have a significant effect on the relationship between entrepreneurial environment and entrepreneurial intention. Third, the degree of self-determination was found to moderate the relationship between creativity, problem solving, communication, marketing and business plan of entrepreneurial competency and entrepreneurial intention. Specifically, as the degree of self-determination increases, the size of the influence of creativity, problem solving, marketing, and business plan on entrepreneurial intention plays a role of strengthening in a positive direction. On the other hand, as the degree of self-determination increases, the degree of self-determination, which weakens the relationship between communication and entrepreneurial intention. Future research will require exploration of other factors that can explain entrepreneurial environment and entrepreneurial capacity, and follow-up studies are needed to analyze the moderated mediating effects through conditional process models that include new mediating and moderating variables.