• Korean Journal of Culture and Social Issue
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• v.22 no.1
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• pp.65-85
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• 2016

In this study, we examined the longitudinal change of multicultural acceptability, peer relationship, and teacher relationship using latent growth curve modeling. This study used data from the second, third, and fourth waves of the middle school student cohort (N=2,178) of the Korean Children-Youth Panel Survey (KCYPS). The dependent variable was multicultural acceptability. The independent variables were peer relationship and teacher relationship. The major longitudinal findings of this study are as follows: First, peer relationships, teacher relationships and multicultural acceptability increased with time. Second, peer relationship showed a significant effect on the multicultural acceptability over time. Finally, the teacher relationship showed a significant effect on the multicultural acceptability over time. These results show differentiation with previous cross-sectional studies of multicultural acceptability. Furthermore, it is expected that this study will provide educational implications for the cultivation of multicultural acceptability.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• Journal of Distribution Science
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• v.14 no.2
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• pp.93-101
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• 2016

Purpose - This study aims to advance our knowledge about factors influencing technical startup performance through analysing technical startup process empirically. This study was conducted to focus on industry structure(industry growth rate, competitive intensity, and enter barriers), technology characteristics(technical excellence and wide range of technical application), and the performance in the technology-based start-ups. Specifically, analyzing moderating effect of technology-marketing strategy, this studied how moderating variables affect technical startup performance under industry structure. Research design, data, and methodology - The subject of this study was technology-based start-ups company that received technology transfer from public organization. The development of the paper model is based on the literature of the preceding research analysis in technology commercialization, performance of technology-based start-ups, and marketing strategy. This study has a construct that was defined in the previous studies, such that technology marketing strategy was defined into the two ways of being broad or narrow in strategic application. From November 3. 2015 to December 22, 220 questionnaires were distributed with targeting to start-up companies in technology-based. 188 responses were collected for empirical analysis except the missing and wrong value responses. This data were used for structural equation modeling and regression analysis. Results - The results of this study are as follows. First, as industry structure variables influencing on performance(technical, financial) of technology-based start-ups, industry growth rate, competitive intensity and enter barriers of variables were verified; high growth rate has more positive effect on performance than low growth rate, competitive low intensity has more positive effect on performance than competitive high intensity, low enter barriers have more positive effect on performance than high enter barriers. Second, as technology characteristics variables influences on the performance(technical, financial) of technology-based start-ups, technical excellence and wide range of technical application of variables were verified ; technical high-excellence has more positive effect on performance than technology low-excellence, wide range of technical application has more positive effect on performance than narrow range of technical application. We also find that technology marketing strategy(broad/narrow) in moderating factors on performance (technical, financial) is as follows. Analyzing the moderating effect depending on technology marketing strategy(broad/narrow), application of technology, and the types of technology strategy(broad/narrow) were revealed that broad marketing strategy had a more significant effect on performance of technology-based start-ups. With AMOS, the relevancy of the study model revealed higher for broad technology-marketing strategy than narrow technology marketing strategy, and the explanatory power revealed to be 6.4% higher in broad marketing strategy than narrow marketing strategy. Conclusions - This study confirmed that industry structure and technology characteristics are important factors influencing the performance of technology-based start-ups. Technology-marketing strategy affects the performance of technology-based start-ups between industry structure and technology characteristics. According to additional analysis, moderating variables and technology-marketing strategy are important factors influencing the performance of technology-based start-ups under industry structure and technology characteristics. Broad type of technology-marketing strategy has more attractive industry structure and excellent technology characteristics than narrow types of technology-marketing.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1323-1329
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• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Education of Primary School Mathematics
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• v.23 no.4
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• pp.207-227
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• 2020

The demand for private education in Korea is steadily increasing every year, and the participation rate of private education is increasing as the grade goes down. In order to empirically verify the effectiveness of private education, it is necessary to analyze through longitudinal data that has been mainly investigated over a long period of time. This study investigated the longitudinal changes in mathematics academic achievement and participation time in mathematics private education using longitudinal data from 2013 (4th grade in elementary school) to 2017 (2nd grade in middle school) of the Seoul Education Longitudinal Study. The students were divided into groups in which mathematics academic achievement changed similarly as the grade went up, and the effect of mathematics academic achievement was examined according to the change of participation time in private mathematics education for each group. As a result of the study, it was found that the participation time of private math education of all students continuously increased from the 5th grade of elementary school to the 2nd grade of middle school, and the participation time of private math education by group was different. In addition, the effect of private tutoring by group was different according to the group.

• Communications of Mathematical Education
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• v.35 no.1
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• pp.97-118
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• 2021

Since the characteristics of teachers that affect mathematics academic achievement are constantly changing and affecting mathematics achievement, longitudinal studies that can predict and analyze growth are needed. This study used data from middle and high school students from 2013(first year of middle school) to 2017(second year of high school) of the Seoul Education Longitudibal Study(SELS). By classifying the longitudinal changes in mathematics academic achievement into similar subgroups, the direct influence of teachers' characteristics(professionalism, expectations, academic feedback) perceived by students on the longitudinal changes in mathematics academic achievement was examined. As a result of the study, it was found that the characteristics of mathematics teachers(professional performance, expectation, and academic feedback) in group 1(343 students), which included the top 14.5% of students, did not directly affect longitudinal changes in mathematics academic achievement. Students in the middle 2nd group(745, 32.2%) had academic feedback from the mathematics teacher, and the 2nd group(1225 students) in the lower 53%, which included most of the students, showed that the expectations of the mathematics teacher were the longitudinal mathematics achievement. The change has been shown to have a direct effect. This suggests that support for teaching and learning should also reflect this, as the direct influence of teachers' professionalism, expectations, and academic feedback on longitudinal changes in mathematics academic achievement is different according to the characteristics and dispositions of students.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.525-544
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• 2020

There are many factors that affect academic achievement, and the influences of those factors are also complex. Since the factors that influence mathematics academic achievement are constantly changing and developing, longitudinal studies to predict and analyze the growth of learners are needed. This study uses longitudinal data from 2014 (second year of middle school) to 2017 (second year of high school) of the Seoul Education Longitudibal Study, and divides it into groups with similar longitudinal patterns of change in mathematics academic achievement. The longitudinal change patterns and direct influence of mood and satisfaction were examined. As a result of the study, it was found that the mathematics academic achievement of the first group (1456 students, 68.3%) including the majority of students and the second group (677 students) of the top 31.7% had a direct influence on the mathematics class attitude. It was found that the mood and satisfaction of mathematics classes did not have a direct effect. In addition, the influence of mathematics class attitude on mathematics academic achievement was different according to the group. In addition, students in group 2 with high academic achievement in mathematics showed higher mathematics class attitude, mood, and satisfaction. In addition, the attitude, atmosphere, and satisfaction of mathematics classes were found to change continuously from the second year of middle school to the second year of high school, and the extent of the change was small.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.2
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• pp.308-323
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• 2021

In this study, we aimed to evaluate the environmental changes in Jinhae Bay caused by cooling water using numerical modeling. Cooling water discharge volume from the results of Case 1 (10 m³ sec^-1) showed that the environmental changes in Jinhae Bay were extremely insignificant throughout the study period. In the simulation conditions of Case 2 (100 m³ sec^-1), there was a decrease in water temperature of approximately 1 - 3℃ within a 5 km radius from the discharge outlet. In Case 3 (1000 m³ sec^-1), a decrease in water temperature of up to 4 - 5℃ was observed within a radius of 8 km from the discharge outlet and cooling water discharge spread throughout the Bay. Growth rate of microalgae decreased by up to 15 % in November, whereas it increased by up to 6 % near the Hangam Bay in Case 3. From the above results, we confirmed that the environmental changes in Jinhae Bay due to cooling water discharged from Tongyeong LNG station are extremely insignificant. Moreover, it is expected that cooling water discharge could be utilized as a counter measure for 'red tide bloom' or 'macroalgae growth'.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.608-623
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• 2023

Purpose: Recently, the number of logistics warehouses has been on the rise. In addition, as the number of such logistics warehouses increases, number of fire accidents also increases every year, increasing the importance of preventing fires in large logistics warehouses. Method: investigated aspirating smoke detectors that are emerging as adaptive fire detectors in logistics warehouses. Then, through fire simulation (FDS), logistics warehouse modeling was conducted to compare and analyze the detection speed of general smoke detectors and aspirating smoke detectors according to four stages of fire growth and three locations of fire in the logistics warehouse. Result: Growth speed in Slow-class fires and Mediumclass fires, the detection speed of aspirating smoke detectors was faster regardless of the location of the fire. However, in Fast-class fires and Ultra-Fast-class fires, it was confirmed that the detection speed of general smoke detectors was faster depending on the location of the fire. Conclusion: It was confirmed that the detection performance of the aspirating smoke detector decreased as the fire growth speed increased and the location of the fire occurred further than the receiver of the aspirating smoke detector. Therefore, even if an aspirating smoke detector is installed in a warehouse that stores combustibles with high fire growth rates, it is judged that an additional smoke detector is attached far away from the receiver of the general smoke detector to increase fire safety.

• Journal of Food Hygiene and Safety
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• v.28 no.1
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• pp.7-12
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• 2013

This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures ($0^{\circ}C$, $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at $0^{\circ}C$ and $5^{\circ}C$, whereas some growth was observed at $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$, with a mean increase of only 0.34 log CFU/g. The growth observed at $20^{\circ}C$ was more robust than that observed at $15^{\circ}C$, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated $R^2$ values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the ${\pm}0.5$ log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.