• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.71-86
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• 2023

Although starting a business plays a key role in strengthening national competitiveness and creating jobs, it is recognized as a risky choice. Failure to start a business can result in a wide range of negative effects, such as loss of personal wealth as well as deterioration of national competitiveness. This study considers startups that have reached a level of sustainable growth by achieving performance above the minimum profitability and sales standards for KOSDAQ listing, or achieved EXIT through sale or listing, as successful startups. based on the practical experiences of 23 successful entrepreneurs and Based on perception, the importance and priorities of startup success factors were derived through stratification analysis (Analytic Hierarchy Process, AHP), and interviews were conducted. In particular, using the ERIS model, we comprehensively analyze various variables of a start-up by considering the four elements of the entrepreneur, resources, industry, and strategy, and examine the changes and importance of success factors according to the characteristics of each growth stage of the start-up. As a goal, we specifically identified the challenges and opportunities faced by entrepreneurs at each stage. As a result of the study, the order of importance of the top factors of success factors in the start-up period was found to be the entrepreneur, resources, industry, and strategy. In particular, the importance of the entrepreneur's entrepreneurship spirit, special capabilities, general capabilities, and human resources was emphasized. The order of importance of the top factors of success factors during the growth period was found in the following order: entrepreneur, resources, industry, and strategy. In particular, the importance of general capabilities, entrepreneurship, and human and organizational resources was emphasized. This study is significant in that it analyzes startup success factors from the perspective of successful entrepreneurs and provides useful insights and directions to entrepreneurs and policy makers.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.6
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• pp.147-157
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• 2023

This paper investigates the financial factors affecting angel investors' investment decisions for 818 firms from 2009 to 2018 in the Korean venture investment market. We construct a quasi-experimental design using propensity scoring matching and compare the investment determinants between investment firms and matching firms. The main empirical findings are as follows. First, we find that angel investors are more likely to choose firms based on a firm's growth such as profit and assets rather than profitability or financial stability. In addition, we identify that they prefer the firm not only higher intangible assets but also higher R&D expenditures. Second, we find that angel investors consider both growth and activity ratios in the firms for over three years and have entered the mid-stage of startups. Overall, we confirm that the investment decision of angel investors mainly focuses on the venture startups' growth trend or future growth potential rather than the realized profitability or financial stability. We also infer that the possibility of performance creation is an important investment factor along with growth for the mid-stage startup.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of the Korean earth science society
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• v.45 no.2
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• pp.111-120
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• 2024

Cloud coverage is a key factor in determining whether to proceed with observations. In the past, human judgment played an important role in weather evaluation for observations. However, the development of remote and robotic observation has diminished the role of human judgment. Moreover, it is not easy to evaluate weather conditions automatically because of the diverse cloud shapes and their rapid movement. In this paper, we present the development of a cloud monitoring program by applying a machine learning-based Python module "cloudynight" on all-sky camera images obtained at Miryang Arirang Astronomical Observatory (MAAO). The machine learning model was built by training 39,996 subregions divided from 1,212 images with altitude/azimuth angles and extracting 16 feature spaces. For our training model, the F1-score from the validation samples was 0.97, indicating good performance in identifying clouds in the all-sky image. As a result, this program calculates "Cloudiness" as the ratio of the number of total subregions to the number of subregions predicted to be covered by clouds. In the robotic observation, we set a policy that allows the telescope system to halt the observation when the "Cloudiness" exceeds 0.6 during the last 30 minutes. Following this policy, we found that there were no improper halts in the telescope system due to incorrect program decisions. We expect that robotic observation with the 0.7 m telescope at MAAO can be successfully operated using the cloud monitoring program.

• Tunnel and Underground Space
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• v.34 no.3
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• pp.231-247
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• 2024

In the field of high-level radioactive waste disposal targeting deep rock environments, hydraulic characteristic information serves as the most important key factor in selecting relevant disposal sites, detailed design of disposal facilities, derivation of optimal construction plans, and safety evaluation during operation. Since various rock types are mixed and distributed in a small area in Korea, it is important to conduct preliminary work to analyze the hydrogeological characteristics of rock aquifers for various rock types and compile the resulting data into a database. In this paper, we obtained hydraulic conductivity data, which is the most representative field hydraulic characteristic of a high-depth volcanic bedrock aquifer, and also analyzed and evaluated the field data. To acquire field data, we used a high-performance hydraulic testing system developed in-house and applied standardized test methods and investigation procedures. In the process of hydraulic characteristic data analysis, hydraulic conductivity values were obtained for each depth, and the pattern of groundwater flow through permeable rock joints located in the test section was also evaluated. It is expected that the series of data acquisition methods, procedures, and analysis results proposed in this report can be used to build a database of hydraulic characteristics data for high-depth rock aquifers in Korea. In addition, it is expected that it will play a role in improving technical know-how to be applied to research on hydraulic characteristic according to various bedrock types in the future.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Information Systems Review
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• v.21 no.4
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• pp.47-68
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• 2019

As cloud services and deployment models become diverse, there are a growing number of cloud computing selection options. Therefore, financial companies need a methodology to select the appropriated cloud for each financial computing system. This study adopted the Balanced Scorecard (BSC) framework to classify factors for the introduction of cloud computing in financial companies. Using Analytic Hierarchy Process (AHP), the evaluation items are layered into the performance perspective and the cloud consideration factor and a comprehensive decision model is proposed. To verify the proposed research model, a system of financial company is divided into three: account, information, and channel system, and the result of decision making by both financial business experts and technology experts from two financial companies were collected. The result shows that some common factors are important in all systems, but most of the factors considered are very different from system to system. We expect that our methodology contributes to the spread of cloud computing adoption.

• Journal of The Korean Society of Clinical Toxicology
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• v.21 no.2
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• pp.135-142
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• 2023

Purpose: In patients with glufosinate poisoning, severe neurological symptoms may be closely related to a poor prognosis, but their appearance may be delayed. Therefore, this study aimed to determine whether the Acute Physiology and Chronic Health Evaluation II (APACHE II) score could predict the neurological prognosis in patients with glufosinate poisoning who present to the emergency room with alert mental status. Methods: This study was conducted retrospectively through a chart review for patients over 18 years who presented to a single emergency medical center from January 2018 to December 2022 due to glufosinate poisoning. Patients were divided into groups with a good neurological prognosis (Cerebral Performance Category [CPC] Scale 1 or 2) and a poor prognosis (CPC Scale 3, 4, or 5) to identify whether any variables showed significant differences between the two groups. Results: There were 66 patients (67.3%) with good neurological prognoses and 32 (32.8%) with poor prognoses. In the multivariate logistic analysis, the APACHE II score, serum amylase, and co-ingestion of alcohol showed significant results, with odds ratios of 1.387 (95% confidence interval [CI], 1.027-1.844), 1.017 (95% CI, 1.002-1.032), and 0.196 (95% CI, 0.040-0.948), respectively. With an APACHE II score cutoff of 6.5, the AUC was 0.826 (95% CI, 0.746-0.912). The cutoff of serum amylase was 75.5 U/L, with an AUC was 0.761 (95% CI, 0.652-0.844), and the AUC of no co-ingestion with alcohol was 0.629 (95% CI, 0.527-0.722). Conclusion: The APACHE II score could be a useful indicator for predicting the neurological prognosis of patients with glufosinate poisoning who have alert mental status.

• Journal of Physiology & Pathology in Korean Medicine
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• v.38 no.1
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• pp.1-9
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• 2024

Cognitive reserve (CR) is a concept that can explain the discrepancies between the pathologic burden of the disease and clinical manifestations. It refers to the individual susceptibility to age-related brain changes and pathologies related to Alzheimer's disease, thus recognized as a factor affecting the trajectories of the disease. The purpose of this study was to explore the current states of clinical studies on neural substrates of CR in Alzheimer's disease using functional magnetic resonance imaging. We searched for clinical studies on CR using fMRI in the Pubmed, Cochrane library, RISS, KISS and ScienceON on August 14, 2023. Once the online search was finished, studies were selected manually by the inclusion criteria. Finally, we analyzed the characteristics of selected articles and reviewed the neural substrates of CR. Total thirty-four studies were included in this study. As surrogate markers of CR, not only education and occupational complexity, but also composite score and questionnaire-based method, which cover various areas of life, were mainly used. The most utilized methods in resting-state fMRI were independent component analysis, seed-based analysis, and graph theory analysis. Through the analysis, we demonstrated that neuroimaging techniques could capture the neural substrates associated with cognitive reserve. Moreover, functional connectivity of brain regions centered on prefrontal and parietal cortex and network areas such as default mode network showed a significant correlation with CR, which indicated a significant association with cognitive performance. CR may induce differential effects according to the disease status. We hope that this perspective on cognitive reserve would be helpful when conducting clinical researches on the mechanisms of traditional Korean medicine for Alzheimer's disease in the future.

• Tunnel and Underground Space
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• v.34 no.1
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• pp.15-27
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• 2024

In this case study, detailed survey of the Excavation Damaged Zone (EDZ) evaluation for the deep geological repository for high level nuclear waste was conducted. Oversea and Domestic case studies were compiled and investigated. EDZ is considered a crucial factor in the performance assessment of spent fuel disposal, leading to numerous studies worldwide aiming to understand the characteristics of the EDZ and quantitatively assessment of its extent through field and laboratory tests at Underground Research Laboratory (URL) sites. To enhance the understanding of EDZ, this study begins with defining and exploring the history of EDZ, compiling factors influencing EDZ, and summarizing the impacts caused by EDZ. Subsequently, an analysis of EDZ and rock properties is performed, followed by presenting generalized outcomes, limitations drawn from previous research, and proposing future research directions.