• Archives of Plastic Surgery
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• v.37 no.6
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• pp.721-725
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• 2010

Purpose: The survival of composite graft is dependent on three steps, (1) plasmatic imbibitions, (2) inosculation, and (3) neovascularization. Among the many trials to increase the survival rate of composite graft, prostaglandin E1 (PGE1) has beneficial effects on the microcirculatory level with vasodilating, antithrombotic, anti-inflammatory and neoangiogenic properties. Lipo-PGE1 which is lipid microspheres containing PGE1 had developed to compensate the systemic and local side effects of PGE1. This study was proposed to determine whether Lipo-PGE1 administration enhanced the survival of composite graft through neovascularization quantitatively in a rabbit ear model. Methods: Fourteen New Zealand White Rabbits each weighing 3~4 kg were divided in two groups: (1) intravenous Lipo-PGE1 injection group and (2) control group. A $2{\times}1\;cm$ sized, full-thickness rectangular composite graft was harvested in each auricle. Then, the graft was reaaproximated in situ using a 5-0 nylon suture. For the experimental group, $3{\mu}g$/kg/day of Lipo-PGE1 ($5{\mu}g$/mL) was administered intravenously through the marginal vein of the ear for 14 days. The control group was received no pharmacologic treatment. On the 14th postoperative day, composite graft of the ear was harvested and immunochemistry staining used Monoclonal mouse anti-CD 31 antibody was performed. Neoangiogenesis was quantified by counting the vessels that showed luminal structures surrounded by the brown color-stained epithelium and counted from 10 random high-power fields (400x) by independent blinded observer. Statistical analysis (Wilcoxon Signed Ranks test for nonparametric data) was performed using SPSS v12.0, with values of p<0.05 considered significant. Results: The mean number of the microvessels was $15.48{\pm}8.65$ in the experimental group and $9.82{\pm}7.25$ in the control group (p=0.028). Conclusion: The use of Lipo-PGE1 facilitated the neoangiogenesis, resulted in the improvement of the survival rate of graft. On the basis of this results, we could support wider application of Lipo-PGE1 for more effective therapeutic angiogenesis and successful survival in various cases of composite graft in the human.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.74-82
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• 2016

The term research institute spin-offs refers to new firms created by public research institutes. These spin-offs are different from other start-ups in two respects: on the one hand, they should be located in the Special Research and Development Zones and, on the other hand, these firms are supposed to commercialize the results of public R&D activities. These spin-off firms show higher rates of survival and job creation than general new firms, which means that their contribution to economic growth is not negligible. The present study analyzes the factors affecting research institute spin-offs using a random effect panel logit model and negative binomial model. From previous studies, four elements are identified as playing an important role in the commercialization of public R&D through spin-offs, namely their organizational character, research capability, technological character, and geographical location. The empirical results demonstrate that government research institutes with more researchers and patents are more likely to create new firms. In addition, the location of the institutes significantly affects the probability of their creating spin-offs and their number. When the technological stage and TLO size are considered, however, it turns out that the number of researchers and technological stage play important roles in the spin-offs.

• Journal of health informatics and statistics
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• v.43 no.4
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• pp.290-299
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• 2018

Objectives: This study aimed to evaluate the change in weight and heart rate associated with the use of phentermine through meta-analysis based on the published literatures. Methods: Eight electronic databases, PubMed, EMBASE, Cochrane library, and five domestic databases were used to search the literature. Randomized controlled trials that evaluated the change in weight and heart rate with the use of phentermine compared with placebo were included in this study. The fixed-effect model weighted by the Mantel-Haenszel method was used in the meta-analysis, and the random-effects model was used when heterogeneity was present. Results: We included 12 studies comprising 677 patients. The change in weight observed with the use of phentermine (SMD = -1.37, 95% CI: -1.55, -1.19) was statistically significant compared with that observed with placebo. As per the subgroup analysis results, the change in weight by publication year, country, phentermine dosage, follow-up check was not heterogeneous. The change in heart rate observed with the use of phentermine (SMD = 0.64, 95% CI: 0.35, 0.92) was significant compared with that observed with placebo. Conclusions: Weight loss and increased heart rate were confirmed in phentermine compared with placebo.

• Animal Bioscience
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• v.34 no.3_spc
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• pp.434-442
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• 2021

Objective: This study aims to evaluate the influence of dietary flavonoids on the growth performance, blood and intestinal profiles, and carcass characteristics of broilers by employing a meta-analysis method. Methods: A database was built from published studies which have reported on the addition of various levels of flavonoids from herbs into broiler diets and then monitored growth performance, blood constituents, carcass proportion and small intestinal morphology. A total of 42 articles were integrated into the database. Several forms of flavonoids in herbs were applied in the form of unextracted and crude extracts. The database compiled was statistically analyzed using mixed model methodology. Different studies were considered as random effects, and the doses of flavonoids were treated as fixed effects. The model statistics used were the p-values and the Akaike information criterion. The significance of an effect was stated when its p-value was <0.05. Results: Dietary flavonoids increased (quadratic pattern; p<0.05) the average daily gain of broilers in the finisher phase. There was a reduction (p<0.01) in the feed conversion ratio of the broilers both in the starter (linear pattern) and finisher phases (quadratic pattern). The mortality rate tended to decrease linearly (p<0.1) with the addition of flavonoids, while the carcass parameter was generally not influenced. A reduction (p<0.001) in cholesterol and malondialdehyde concentrations (both linearly) was observed, while super oxide dismutase activity increased linearly (p<0.001). Increasing the dose of flavonoids increased (p<0.01) the villus height (VH) and villus height and crypt depth (VH:CD) ratio (p<0.05) in the duodenum. Similarly, the VH:CD ratio was elevated (p<0.001) in the jejunum following flavonoid supplementation. Conclusion: Increasing levels of flavonoids in broilers diet leads to an improvement in growth performance, blood constituents, carcass composition and small intestinal morphology.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.967-977
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• 2022

The occurrence and intensity of wildfires are increasing with climate change. Emissions from forest fire smoke are recognized as one of the major causes affecting air quality and the greenhouse effect. The use of satellite product and machine learning is essential for detection of forest fire smoke. Until now, research on forest fire smoke detection has had difficulties due to difficulties in cloud identification and vague standards of boundaries. The purpose of this study is to detect forest fire smoke using Level 1 and Level 2 data of Geostationary Environment Monitoring Spectrometer (GEMS), a Korean environmental satellite sensor, and machine learning. In March 2022, the forest fire in Gangwon-do was selected as a case. Smoke pixel classification modeling was performed by producing wildfire smoke label images and inputting GEMS Level 1 and Level 2 data to the random forest model. In the trained model, the importance of input variables is Aerosol Optical Depth (AOD), 380 nm and 340 nm radiance difference, Ultra-Violet Aerosol Index (UVAI), Visible Aerosol Index (VisAI), Single Scattering Albedo (SSA), formaldehyde (HCHO), nitrogen dioxide (NO₂), 380 nm radiance, and 340 nm radiance were shown in that order. In addition, in the estimation of the forest fire smoke probability (0 ≤ p ≤ 1) for 2,704 pixels, Mean Bias Error (MBE) is -0.002, Mean Absolute Error (MAE) is 0.026, Root Mean Square Error (RMSE) is 0.087, and Correlation Coefficient (CC) showed an accuracy of 0.981.

• Journal of Environmental Health Sciences
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• v.48 no.4
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• pp.227-235
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• 2022

Background: Abandoned metal mines are classified as vulnerable areas with the highest level of soil contamination among risk regions. People living near abandoned metal mines are at increased risk of exposure to toxic metals. Objectives: This study aimed to evaluate the correlation between soil contamination levels in areas around abandoned metal mine and the blood lead levels of local residents. Moreover, we assess the possibility of using soil contamination levels as a predictive indicator for human exposure level. Methods: Data from the Survey of Residents around Abandoned Metal Mines (2013~2017, n=4,421) and Investigation of Soil Pollution in Abandoned Metal Mines (2000~2011) were used. A random coefficient model was conducted for estimation of the lower level (micro data) of the local resident unit and the upper level (macro data) of the abandoned metal mine unit. Through a fitted model, the variation of blood lead levels among abandoned metal mines was confirmed and the effect of the operationally defined soil contamination level was estimated. Results: Among the total variation in blood lead levels, the variation between abandoned mines was 18.6%, and the variation determined by the upper-level factors such as soil contamination and water contamination was 8.1%, which was statistically significant respectively. There was also a statistically significant difference in the least square mean of blood lead concentration according to the level of soil contamination (p=0.047, low: 2.32 ㎍/dL, middle: 2.38 ㎍/dL, high: 2.59 ㎍/dL). Conclusions: The blood lead concentration of residents living near abandoned metal mines was significantly correlated with the level of soil contamination. Therefore, in biomonitoring for vulnerable areas, operationally defined soil contamination can be used as a predictor for human exposure level to hazardous substances and discrimination of high-risk abandoned metal mines.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Venture Innovation
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• v.3 no.2
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• pp.41-76
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• 2020

This study confirmed factors affecting smart factory technology acceptance through empirical analysis. It is a study on what factors have an important influence on the introduction of the smart factory, which is the core field of the 4th industry. I believe that there is academic and practical significance in the context of insufficient research on technology acceptance in the field of smart factories. This research was conducted based on the Unified Theory of Acceptance and Use of Technology (UTAUT), whose explanatory power has been proven in the study of the acceptance factors of information technology. In addition to the four independent variables of the UTAUT : Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions, Government Assistance Expectancy, which is expected to be an important factor due to the characteristics of the smart factory, was added to the independent variable. And, in order to confirm the technical factors of smart factory technology acceptance, the Task Technology Fit(TTF) was added to empirically analyze the effect on Behavioral Intention. Trust is added as a parameter because the degree of trust in new technologies is expected to have a very important effect on the acceptance of technologies. Finally, empirical verification was conducted by adding Innovation Resistance to a research variable that plays a role as a moderator, based on previous studies that innovation by new information technology can inevitably cause refusal to users. For empirical analysis, an online questionnaire of random sampling method was conducted for incumbents of domestic small and medium-sized enterprises, and 309 copies of effective responses were used for empirical analysis. Amos 23.0 and Process macro 3.4 were used for statistical analysis. For accurate statistical analysis, the validity of Research Model and Measurement Variable were secured through confirmatory factor analysis. Accurate empirical analysis was conducted through appropriate statistical procedures and correct interpretation for causality verification, mediating effect verification, and moderating effect verification. Performance Expectancy, Social Influence, Government Assistance Expectancy, and Task Technology Fit had a positive (+) effect on smart factory technology acceptance. The magnitude of influence was found in the order of Government Assistance Expectancy(β=.487) > Task Technology Fit(β=.218) > Performance Expectancy(β=.205) > Social Influence(β=.204). Both the Task Characteristics and the Technology Characteristics were confirmed to have a positive (+) effect on Task Technology Fit. It was found that Task Characteristics(β=.559) had a greater effect on Task Technology Fit than Technology Characteristics(β=.328). In the mediating effect verification on Trust, a statistically significant mediating role of Trust was not identified between each of the six independent variables and the intention to introduce a smart factory. Through the verification of the moderating effect of Innovation Resistance, it was found that Innovation Resistance plays a positive (+) moderating role between Government Assistance Expectancy, and technology acceptance intention. In other words, the greater the Innovation Resistance, the greater the influence of the Government Assistance Expectancy on the intention to adopt the smart factory than the case where there is less Innovation Resistance. Based on this, academic and practical implications were presented.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.4
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• pp.65-76
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• 2014

The basic purpose of this study is to examine the relationship among entrepreneurial intention, university supports for startup, and startup activities of university students. For the study, we identified the influence factors of students' startup intention based on reviewing preceding studies and examined how these factors affect their intention of new venture startup. In addition, this study attempted to examine how these factors that can have a significant impact on entrepreneurial intention affect startup activities and analyzed how entrepreneurial intention would mediate the relationship between these influence factors and startup activities. A total of 769 students who chosen by random were surveyed and all questionnaires were sent by mail to the universities that entrepreneurship education and entrepreneurial programs were selected as the forerunners from the government. As a result, this study revealed that student's psychological traits such as entrepreneurial self-efficacy and risk-taking have significant effect on the intention of startup. And student's exposure to the role models and various entrepreneurial experiences such as entrepreneurship education and entrepreneurial student's club in the university has significantly positive influence on the intention of startup. This study also found that the effects of these explanatory variables of this research on startup activities have been partially mediated by entrepreneurial intention. The entrepreneurial intention was also proven to have a significant effect on startup activities. Finally, the extent to which university supports activities for students' startup moderated the relationship between entrepreneurial intention and university students' startup activities. We believe that these results of this study contribute to the understanding of the entrepreneurship process both theoretical and practical perspectives.