• Journal of health informatics and statistics
• /
• v.42 no.4
• /
• pp.309-316
• /
• 2017

Objectives: Radon and its progeny pose environmental risks as a carcinogen, especially to the lungs. Investigating factors affecting indoor radon concentrations and models thereof are needed to prevent exposure to radon and to reduce indoor radon concentrations. The purpose of this study was to identify factors affecting indoor radon concentration and to construct a comprehensive model thereof. Methods: Questionnaires were administered to obtain data on residential environments, including building materials and life style. Decision tree and structural equation modeling were applied to predict residences at risk for higher radon concentrations and to develop the comprehensive model. Results: Greenery ratio, impermeable layer ratio, residence at ground level, daily ventilation, long-term heating, crack around the measuring device, and bedroom were significantly shown to be predictive factors of higher indoor radon concentrations. Daily ventilation reduced the probability of homes having indoor radon concentrations ${\geq}200Bq/m^3$ by 11.6%. Meanwhile, a greenery ratio ${\geq}65%$ without daily ventilation increased this probability by 15.3% compared to daily ventilation. The constructed model indicated greenery ratio and ventilation rate directly affecting indoor radon concentrations. Conclusions: Our model highlights the combined influences of geographical properties, groundwater, and lifestyle factors of an individual resident on indoor radon concentrations in Korea.

• Journal of Digital Convergence
• /
• v.19 no.5
• /
• pp.175-186
• /
• 2021

The roles and functions of domestic public libraries are diversifying, but various problems have emerged due to internally biased book lending. In addition, due to the 4th Industrial Revolution, public libraries have introduced a book recommendation system focusing on popular books, but the variety of books that users can access is limited. Therefore, in this study, the public library unborrowed book recommendation system was implemented limiting its spatial scope to Duryu Library in Daegu City to enhance the satisfaction of public library users, by using the loan records data (213,093 cases), user information (35,561 people), etc. and utilizing methods like cluster analysis, topic modeling, content-based filtering recommendation algorithm, and conducted a survey on actual users' satisfaction to present the possibility and implications of the unborrowed book recommendation system. As a result of the analysis, the majority of users responded with high satisfaction, and was able to find the satisfaction was relatively high in the class classified by specific gender, age, occupation, and usual reading. Through the results of this study, it is expected that some problems such as biased book lending and reduced operational efficiency of public libraries can be improved, and limitations of the study was also presented.

• Journal of The Korean Association For Science Education
• /
• v.38 no.6
• /
• pp.875-883
• /
• 2018

This study examined overall effect of STEAM programs on student learning through meta-analysis of journal articles published for the past six years. We examined the areas of effects that the research tested and analyzed overall effect across the research. We first identified academic journal articles that utilized quasi-experimental design in examining STEAM effects on student learning and presented appropriate data for meta-analysis such as effect size. A total of 63 articles were identified to be appropriate for meta-analysis. Using R packages, we first identified outliers and eliminated them in the analysis of mean effect size. Thus, 172 effect sizes from 60 studies were analyzed. The results showed that the mean effect was medium (effect size = 0.52). Analysis showed that moderators of the effect were affective measures, thinking skills, character measures, and career aspirations, which meant the studies that measured these variables had more effect than achievement measures. On the other hand, the school level (elementary, middle, and high school), the absence or presence of student products as program requirements, hours of intervention, and sample size did not moderate the effect. Thus, regardless of these variables STEAM programs produced medium effect in general. Based on these results, further research areas and topics are suggested.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.872-877
• /
• 2018

STEAM is an acronym for Science, Technology, Engineering, Arts, and Mathematics. It is considered important to equip students with a creative thinking ability and the core competences required in future society, helping them devise new ideas emerging from branches of study. This study is about the convergence of instructional design in private finance for the life sciences, which aims to foster talent through problem-based learning (PBL). Skills like collaboration, creativity, critical thinking, and problem solving are part of any STEAM PBL, and are needed for students to be effective. STEAM projects give students a chance to problem-solve in unique ways, because they are forced to use a variety of methods to solve problems that pop up during these types of activities. The results of this study are as follows. First is the structured process of convergence lessons. Second is the convergence lesson process. Third is the development of problems in the introduction of private finance and the life sciences for a convergence lesson at Dornod University. Learning motivation shows the following results: understanding of learning content (66.6%), effectiveness (63.3%), self-directed learning (59.9%), motivation (63.2%), and confidence (63.3%). To make an effective model, studies applying this instructional design are to be implemented.

• Journal of the Korean Society of Earth Science Education
• /
• v.14 no.1
• /
• pp.33-47
• /
• 2021

This study developed a big data-based STEAM (Science, Technology, Engineering, Art & Mathematics) program using WWT (World Wide Telescope), focusing on content elements of 'solar system', 'star and universe' in the 2015 revised science curriculum, and in order to find out the effectiveness of the STEAM program, analyzed creative problem solving, STEAM attitude, and STEAM satisfaction by applying it to one middle school 176 students simple random sampled. The results of this study are as follows. First, we developed a program to encourage students to actively and voluntarily participating, utilizing the astronomical data platform WWT. Second, in the paired t-test based on the difference between the pre- and post-scores of the creative problem solving measurement test, significant statistical test results were shown in 'idea adaptation', 'imaging', 'analogy', 'idea production' and 'elaboration' sub-factors except 'attention task' sub-factor (p < .05). Third, in the paired t-test based on the difference between the pre- and post-scores of the STEAM attitude test, significant statistical test results were shown in 'interest', 'communication', 'self-concept', 'self-efficacy' and 'science and engineering career choice' sub-factors except 'consideration' and 'usefulness / value recognition' sub-factors (p < .05). Fourth, in the STEAM satisfaction test conducted after class application, the average values of sub-factors were 3.16~3.90. The results indicated that students' understanding and interest in the science subject improved significantly through the big data-based STEAM program using the WWT.

• Microbiology and Biotechnology Letters
• /
• v.49 no.1
• /
• pp.75-87
• /
• 2021

Type I Interferons (IFNα) are known for their role as biological anticancer agents owing to their cell-apoptosis inducing properties. Development of an appropriate, cost-effective host expression system is crucial for meeting the increasing demand for proteins. Therefore, this study aims to develop codon-optimized IFNα-2b in L. lactis NZ3900. These cells express extracellular protein using the NICE system and Usp₄₅ signal peptide. To validate the mature form of the expressed protein, the recombinant IFNα-2b was screened in a human colorectal cancer cell line using the cytotoxicity assay. The IFNα-2b was successfully cloned into the pNZ8148 vector, thereby generating recombinant L. lactis pNZ8148-SP_Usp45-IFNα-2b. The computational analysis of codon-optimized IFNα-2b revealed no mutation and amino acid changes; additionally, the codon-optimized IFNα-2b showed 100% similarity with native human IFNα-2b, in the BLAST analysis. The partial size exclusion chromatography (SEC) of extracellular protein yielded a 19 kDa protein, which was further confirmed by its positive binding to anti-IFNα-2b in the western blot analysis. The crude protein and SEC-purified partial fraction showed IC₅₀ values of 33.22 ㎍/ml and 127.2 ㎍/ml, respectively, which indicated better activity than the metabolites of L. lactis NZ3900 (231.8 ㎍/ml). These values were also comparable with those of the regular anticancer drug tamoxifen (105.5 ㎍/ml). These results demonstrated L. lactis as a promising host system that functions by utilizing the pNZ8148 NICE system. Meanwhile, codon-optimized usage of the inserted gene increased the optimal protein expression levels, which could be beneficial for its large-scale production. Taken together, the recombinant L. lactis IFNα-2b is a potential alternative treatment for colorectal cancer. Furthermore, its activity was analyzed in the WiDr cell line, to assess its colorectal anticancer activities in vivo.

• Journal of Industrial Convergence
• /
• v.21 no.8
• /
• pp.1-12
• /
• 2023

The purpose of the study is to analyze the impact of blended learning's external classroom formats and internal teaching strategies, which has been implemented in university classes due to COVID-19, on students' academic achievement and learners' perceptions, as well as to provide insights into the desirable direction of online education. The study was conducted during the 1st semester of 2022 at G University, targeting students taking Calculus I. The experimental group consisted of 117 students, while the control group consisted of 707 students. Blended learning, involving a combination of face-to-face classes, online classes, and mixed teaching methods, was implemented, and academic achievement and learner perceptions were assessed. The research findings indicate that compared to solely online classes, adopting a blended learning approach with online classes before the midterm and face-to-face classes afterwards resulted in a decline in academic achievement. The unprepared and simplistic external format of blended learning was found to be ineffective, however, a blended learning model consisting solely of online classes, incorporating a mix of asynchronous and synchronous instruction, demonstrated positive learner perceptions. Additionally, utilizing technology in the teaching strategies yielded positive outcome.

• Journal of Intelligence and Information Systems
• /
• v.27 no.3
• /
• pp.139-156
• /
• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Journal of The Korean Association of Information Education
• /
• v.15 no.3
• /
• pp.483-492
• /
• 2011

'Learning with a robot' is now considered as one of the best candidates for STEAM education, which is recently growing its importance. Most of the 'learning with a robot' programs in elementary schools serve as afterschool classes. The students participating in the afterschool classes are mostly boys who are interested in science and robots. This paper mainly concerns that a robot can be helpful for improving students' interest in STEAM education. We divided the robot utilizable aspects into 5 areas with educational points of view; abstract concept understanding type, structure-oriented type, athletics-oriented type, intelligence-oriented type and value-orientated type. We extracted all robot utilizable subjects and units from elementary school curriculum, and developed lesson plans which can be applicable to regular classes. And we also verified them by applying into an elementary school for 5 months. As the result of the analysis, we can conclude that 'learning with a robot' can encourage students' interest in STEAM, and it is more effective for girls than boys. Finally, we discuss problems that teachers may face in using a robot for regular classes, and make suggestions about the use of a robot for STEAM education.

• Korean Journal of Ichthyology
• /
• v.21 no.4
• /
• pp.262-272
• /
• 2009

Upstream behavior of glass eels was examined in an experimental eel-ladder at a laboratory of the National Fisheries Research and Development Institute from March to May in 2008. The study was made under various environmental factors and conditions affecting the upstream migration of glass eels in order to design a functional ladder that would allow the passage of glass eels. The experimental eel-ladder consisted of an upper freshwater chamber and a lower sea water chamber; glass eels in sea water can move up to the upper freshwater chamber through the slope (eel-ladder) between them. The optimal condition of the eel-ladder was estimated by comparing the number of glass eels that moved upstream depending on various conditions. Since the glass eels actively moved up the slope to river water rather than to reservoir water or tap water, the experiment was realized using river water. A significantly higher number of glass eels moved up during the spring tide than during the neap tide, and during night than during the day. Upstream movement was significantly higher during high tide than during low tide. Glass eels effectively moved up through a slope of less than $30^{\circ}$ and water-flow velocity lower than 0.4 m/sec. The fish preferred a coarser ladder bed covered with small gravels, brush or carpet.