• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.6
• /
• pp.147-157
• /
• 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
• /
• v.26 no.1
• /
• pp.53-62
• /
• 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
• /
• v.45 no.2
• /
• pp.111-120
• /
• 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
• /
• v.34 no.3
• /
• pp.231-247
• /
• 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
• /
• v.25 no.1
• /
• pp.111-128
• /
• 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.

• Fisheries and Aquatic Sciences
• /
• v.26 no.12
• /
• pp.726-737
• /
• 2023

Piperine, the main bioactive component of black pepper (Piper nigrum Linn.), has anti-inflammatory, antifungal, and antibacterial properties. This study evaluated the supplemental effects of piperine or black pepper on innate immunity, growth, feed utilization efficiency, and intestinal morphology in red seabream (Pagrus major). Six experimental diets were formulated, supplementing piperine at 0.0, 0.25, 0.5, 1.0, and 2.0 g/kg levels (Con, P25, P50, P100, and P200) or 1.0 g/kg black pepper (BP100). Juvenile fish (7.6 ± 0.1 g) were randomly stocked into 18 circular tanks (220 L), including 30 fish per tank. Each diet was randomly assigned to triplicate groups, and the feeding trial was conducted for 8 weeks. The results showed that final body weight, specific growth rate, weight gain, and feed utilization efficiency were significantly improved (p < 0.05) when piperine was supplemented into diets at 0.25-2.0 g/kg levels compared to the Con group. Compared to the Con diet, condition factor was significantly increased (p < 0.05) in fish fed with dietary piperine at 0.25-2.0 g/kg or BP100 diet. Serum myeloperoxidase activity was increased (p < 0.05) in P25 and P100 groups and antiprotease activity was increased (p < 0.05) in P100 group compared to the Con group. Significantly higher (p < 0.05) lysozyme activity was observed in P50, P100, P200 and BP100 groups, while total immunoglobulin level was increased in P50, P100 and BP100 groups than Con group. Superoxide dismutase activity was increased (p < 0.05) by dietary piperine at 0.25-2.0 g/kg levels and BP100 diet compared to Con diet. Plasma cholesterol was significantly lower (p < 0.05) in fish fed with piperine (0.5-2.0 g/kg) or BP100 compared to the Con diet. Compared to the Con diet significantly longer (p < 0.05) intestinal villi were observed in fish fed with piperine at 0.25-1.0 g/kg levels, and higher goblet cell count was observed in P25 and BP100 groups. Dietary inclusion of piperine would be a potent immunostimulant in fish diet and the optimum supplementation level would be 0.25-1.0 g/kg.

• Journal of Korean Society for Quality Management
• /
• v.51 no.4
• /
• pp.531-550
• /
• 2023

Purpose: The unique nature of life-and-death healthcare services sets them apart from other service industries. While many studies exist on the relationship between healthcare services and customer satisfaction, most of them focus on mildly ill patients, ignoring the differences between critically ill and non-seriously ill patients. This study discusses the actual quality of healthcare services for patients who are facing life-threatening illnesses and are on life support, as well as their right to protection and dignity. Methods: The survey conducted to 149 patients with the four major illnesses: cancer, heart disease, brain disease and rare and incurable disease, those who have experiences with senior general hospitals. Results: The basic statistics of this study are adequate to represent the four major critical illnesses, and the reliability and validity of this study's hypotheses, which were measured by multiple items, were analyzed, and the internal consistency was judged to be high. In addition, it was found that the convergent validity was good and the discriminant validity was also secured. When examining the goodness of fit of the hypotheses, the SRMR, which is the standardized root mean square of residuals that measures the difference between the covariance matrix of the data variables and the theoretical covariance matrix structure of the model, met the optimal criteria. Conclusion: The academic implications of this study are differentiated from other studies by moving away from evaluating the quality of healthcare services for mildly ill patients and focusing on the rights and dignity of patients with life-threatening illnesses in four senior general hospitals. In terms of academic implications, this study enriches the depth of related studies by demonstrating the right to protection and dignity as a factor of patient-centeredness based on physical environment quality, interaction quality, and outcome quality, which are presented as sub-factors of healthcare quality. We found that the three quality factors classified by Brady and Cronin (2001) are optimized for healthcare quality assessment and management, and that the results of patients' interaction quality assessment can be used to provide a comprehensive quality rating for hospitals. Health and human rights are inextricably linked, so assessing the degree to which rights and dignity are protected can be a superior and more comprehensive measurement tool than traditional health level measures for healthcare organizations. Practical implications: Improving the quality of the physical environment and the quality of outcomes is an important challenge for hospital managers who attract patients with life and death conditions, but given the scale and economics of time, money, and human inputs, improving the quality of interactions and defining them as performance indicators in hospital quality management is an efficient way to create maximum value in the short term.

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

• Korean Journal of Radiology
• /
• v.21 no.4
• /
• pp.402-412
• /
• 2020

Objective: To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. Materials and Methods: We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. Results: Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. Conclusion: Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

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