• Food Science of Animal Resources
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• v.27 no.4
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• pp.392-400
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• 2007

This experiment was conducted to evaluate effects of various periods of rye silage feeding on the growth performance, blood characteristics, and carcass quality of finishing pigs. A total of sixteen [($Landrace{\times}Yorkshire{\times}Duroc$)] pigs (90.26 kg in average initial body weight) were tested in individual cages for a 30 day period. Dietary treatments included 1) CON (basal diet), 2) S10 (basal diet for 20 days and 3% rye silage for 10 days) 3) S20 (basal diet for 10 days and 3% rye silage for 20 days) and 4) S30 (3% rye silage for 30 days). There were no significant differences in the ADG and gain/feed ratio among the treatments(p>0.05), however the ADFI was higher in pigs fed the CON diet than with pigs fed diets with rye silage (p<0.05). The DM digestibility was higher with the S20 diet than with the S30 diet (p<0.05). With regard to blood characteristics, pigs fed rye silage had a significantly reduced cortisol concentration compared to pigs fed the CON diet (p<0.05). The backfat thickness was higher with the CON diet than with the S20 or S30 diets (p<0.05). Regarding the fatty acid contents of the leans, the C18:0 and total SFA were significantly higher with the CON diet than with the other diets (p<0.05). However, the C18:1n9, total MUFA and UFA/SFA levels were significantly lower with the CON diet than the other diets (p<0.05). Regarding the fatty acid contents of fat, the levels of C18:1n9 and MUFA were similar with the S20 and S30 diets, however, these levels were higher than with the CON or S10 diets (p<0.05). In conclusion, feed intake and DM digestibility were affected by rye silage, and the cortisol concentration, backfat thickness and fatty acid composition of pork were positively affected by feeding pigs rye silage.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.

• Journal of Venture Innovation
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• v.7 no.2
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• pp.77-99
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• 2024

Small and medium-sized venture companies and small business owners play an important role in the Korean economy. According to the Ministry of SMEs and Startups' statistics survey in 2019, the proportion of all employees of small and medium-sized venture companies in Korea was 89.8%, which is overwhelmingly higher than that of Japan's 77.8% and Taiwan's 76.6%, so job creation for small and medium-sized venture companies in Korea is absolute. Compared to large companies, it is known that it is difficult for small and medium-sized venture companies in Korea to secure a competitive advantage due to low cost competitiveness, difficulties in R&D/marketing due to lack of capital, difficulties in securing networks, and lack of management know-how. Many scholars, including Kerr and Schumpeter, have studied entrepreneurship since the mid-1950s. Recently, research has been conducted in various fields such as business administration, psychology, and economics. However, research on the personal characteristics of CEOs of small and medium-sized venture companies such as entrepreneurial passion and corporate reputation, which is the overall image of companies, is still insufficient, and comparisons of effects between variables between groups (by industry, by pressure, etc.) have not yet been presented. Entrepreneurial passion plays an important role in promoting new business creation, job creation, and economic growth. Therefore, entrepreneurial Cultivating and promoting passion will also contribute to socioeconomic development. These are government policies, educational programs, and social awareness It can be achieved through improvement, etc., and is especially important in the current market conditions where innovation and growth are important. In conclusion Entrepreneurial passion is an important factor that has a positive effect on all individuals, businesses, and society. Therefore, research on entrepreneurial passion is It is an essential task to maximize these positive effects. In this study, in order to overcome the limitations of existing studies, entrepreneurs' Whether entrepreneurial passion, which is a personal characteristic, affects competitive advantage, absorption capacity and product and process innovation capabilities are Whether there is a mediating effect between entrepreneurial passion and competitive advantage. Finally, in the relationship between entrepreneurial passion and absorption capacity. The corporate reputation verifies whether it has a moderating effect, etc., and compares the influencing factors between variables by industry and pressure. It is intended to be verified empirically. In this study, 2023 conducted by the Small and Medium Business Distribution Center under the Ministry of SMEs and Startups for 312 CEOs of small and medium-sized venture companies and small business owners who participated in the online market support project for small and medium-sized venture companies and small business owners, December 2023 Survey and analysis were conducted from 1st to 20th, and the analysis used Smart PLS 4.0 to determine the impact on the relationship between variables. It was empirically verified. As a result of the study, it was found that entrepreneurial passion had a positive effect on absorption capacity, and the product was found And it was found to have a positive effect on process innovation capability and competitive advantage. In addition, the higher the corporate reputation, the more entrepreneurial it is. It was confirmed that passion had a greater effect on absorption capacity.

• Journal of the Korean Society of Food Science and Nutrition
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• v.36 no.9
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• pp.1148-1160
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• 2007

This study was performed to document the association between eating behaviors and major risk factors for cardiovascular diseases. Those who stated that they ate a daily breakfast for male and female were 88.6% and 96.0%, $1{\sim}2$ times a week were 9.1% and 2.0%, $3{\sim}4$ times a week were 2.3% and 2.0%, respectively. Those who stated that they were overeating of $0{\sim}1$ time a week for man and female were 80.7% and 89.9%, overeating of $2{\sim}3$ times a week were 19.3% and 10.1%, respectively. Those who said that they were regular of meal time for man and female were 38.6%, and 37.4%, sometimes irregular of meal time were 14.8% and 19.2%, irregular of meal time were 46.6% and 43.4%, respectively. Those who said that they were light of eating volume for man and female were 20.5% and 25.3%, moderate of eating volume were 69.3% and 61.6%, heavy of eating volume were 10.2% and 13.1%, respectively. Those who said that they were very fast of eating speed for man and female were 15.9% and 8.1%, fast of eating speed were 51.1% and 34.3%, moderate of eating speed were 4.5% and 20.2%, slow of eating speed were 17.0% and 14.1%, and very slow of eating speed were 11.4% and 23.2%, respectively. Higher frequency of breakfast a week resulted in higher serum total cholesterol and blood sugar for the daily eating group for both genders with women having high LDL-cholesterol and HDL-cholesterol. Both group had high HDL-cholesterol and low blood sugar with less number of overeating, with men having low triglyceride and LDL-cholesterol. With regular meal, both group had low triglyceride, total-cholesterol, atherogenic index, and blood sugar with women having low LDL-cholesterol. For both groups, the triglyceride, total-cholesterol, LDL-cholesterol, atherogenic index, and blood sugar had higher figures for overeating, with men having low HDL-cholesterol and women having high HDL-cholesterol. This study revealed that less number of overeating, regular mealtime, and less volume of food intake are effective in preventing and treating for the cardiovascular diseases.