• Journal of the Korean Society of Marine Environment & Safety
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• v.17 no.3
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• pp.203-218
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• 2011

Long-term trends and distribution patterns of water quality were investigated in the Yeoja Bay of South Sea, Korea from 1976 to 2010. Water samples were collected at 3 stations and physicochemical parameters were analyzed including water temperature, salinity, hydrogen ion concentration (pH), dissolved oxygen (DO), chemical oxygen demand (COD), suspended solids (SS) and nutrients. Spatial distribution patterns of temperature, pH and DO were not clear among stations but the seasonal variations were distinct except ammonium. The trend analysis by principal component analysis (PCA) during 31 years revealed the significant variations in water quality in the study area. Spatial water qualities were discriminated into 2 clusters by PCA; station cluster 1 and 2~3. Annual water qualities were clearly discriminated into 4 clusters by PCA. By this multi-variate analysis, the annual trends were summarized as the followings; water temperature, COD and SS tended to increase from late 1970's, decreased salinity, and increased phosphate from 1991 to 2001 and increased dissolved inorganic nitrogen. Water quality was showed by the input of fresh water same as those of Kyoungin coastal area, Asan coastal area, Choensoo bay, Gunsan coastal and Mokpo coastal area in the Yeoja Bay.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.2
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• pp.91-108
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• 2018

An ecological study on subtidal macrobenthic fauna was conducted from 25 stations in the estuarine area of Wando-Doam Bay, southern coast of Korea during August 2013. A total of 186 species was collected with a mean density of $1,229ind./m^2$ and a mean biomass of $265.7g/m^2$. Polychaetes showed the richest benthic fauna comprising 43% of total fauna, whereas mollusks appeared as density- and biomass-dominant fauna accounted for 45% and 48% of the mean density and biomass, respectively. The number of species and mean faunal density were relatively higher at the stations surrounded by Sinjido, Joyakdo and Gogeumdo showing a gradual decrease toward inner bay stations. Species number and density were negatively correlated with bottom water temperature, but they were positively correlated with both the bottom salinity and DO. The most dominant species in terms of density was a semelid bivalve, Theora fragilis which showed a positive correlation with TOC content of surface sediment and its high density occurred around Gogeum-Sinji-Joyakdo area where dense aquaculture facilities exist. In the bay mouth area, an amphipod species, Eriopisella sechellensis showed its higher density at the stations with low organic content but fine grains. The combination of water temperature, salinity, pH of bottom water, water and sulfur content of the surface sediment could explain 71% of the spatial distribution of macrobenthic fauna from the Bio-Env analysis. From the cluster analysis, the study area consisted of 6 distinct station groups lineated from offshore area toward inner area. Ampharete arctica, Goniada maculata, Eriopisella sechellensis, Theora fragilis, Caprella sp. were identified as the main contributing faunas in classification by the SIMPER analysis. From the value of BPI, the benthic communities at the inner and central Wando-Doam Bay were assessed to be in a normal condition whereas those at the outer Wando harbor and Gogeum-Sinji-Joyakdo area were assessed in a poor or very poor condition due to the high concentration of particulate organic matter might be originated from the nearby dense aquaculture facilities. This study indicated that pristine inner bay has been influenced by the organic material supplied from the outer bay. Thus it is necessary to establish an ecological management plan to reduce organic enrichment of sediment from dense aquaculture facilities in the outer bay.

• Journal of the Korean Society of Earth Science Education
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• v.15 no.2
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• pp.235-248
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• 2022

With the changes in the future educational environment, such as the rapid decline of the school-age population and the expansion of students' choice of curriculum, changes are also required in PCK, the expertise of science teachers. In other words, the categories constituting the existing 'consensus-PCK' and the characteristics of 'science PCK' are not fixed, so more categories and characteristics can be added. The purpose of this study is to explore the potential area of science PCK required to cope with changes in the future educational environment in the form of 'Family Resemblance Science PCK (Family Resemblance-PCK, hereafter)' through Wittgenstein's family resemblance approach. For this purpose, in-depth interviews were conducted with three focus groups. In the focus group in-depth interview, participants discussed how the science PCK required for science teachers in future schools in 2030-2045 will change due to changes in the future society and educational environment. Qualitative analysis was performed based on the in-depth interview, and semantic network analysis was performed on the in-depth interview text to analyze the characteristics of 'Family Resemblance-PCK' differentiated from the existing 'consensus-PCK'. In results, the characteristics of Family Resemblance-PCK, which are newly requested along with changes in role expectations of science teachers, were examined by PCK area. As a result of semantic network analysis of Family Resemblance-PCK, it was found that Family Resemblance-PCK expands its boundaries from the existing consensus-PCK, which is the starting point, and new PCK elements were added. Looking at the aspects of Family Resemblance-PCK, [AI-Convergence Knowledge-Contents-Digital], [Community-Network-Human Resources-Relationships], [Technology-Exploration-Virtual Reality-Research], [Self-Directed Learning-Collaboration-Community], etc., form a distinct network cluster, and it is expected that future science teacher expertise will be formed and strengthened around these PCK areas. Based on the research results, changes in the professionalism of science teachers in future schools and countermeasures were proposed as a conclusion.

• Journal of the Korean Regional Science Association
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• v.39 no.1
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• pp.53-66
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• 2023

Due to COVID-19, the external activities of urban residents have greatly shrunk, causing a lot of damage to the commercial district, such as a decrease in population and sales. The downturn in commercial districts means the collapse of the infrastructure of the national economy, and can have serious side effects on the local economy and individual lives. Therefore, it is necessary to look at the alley commercial area, which is closely related to the national local economy, and pay attention to the damage and stagnation of the alley commercial area where small business owners are concentrated. The purpose of this study is to classify alley commercial districts into growth commercial districts and decline commercial districts by using commercial sales time series data and DTW time series group analysis for the pre- and post-COVID-19 period. The main findings of the study are as follows. First, using the time series data on commercial sales before and after COVID-19, the alley commercial districts were divided into growth commercial districts and decline commercial districts, and it was confirmed that the distribution of growth commercial districts and decline commercial districts was regionally different. Therefore, it is necessary to actively manage commercial districts in areas where many declining commercial districts are distributed, and it is required to prepare policies for each region in consideration of the spatial distribution of declining commercial districts. Second, during the COVID-19 period, face-to-face essential industries, density of guest facilities, and population density negatively affected the sustainability of commercial districts, which is the opposite of previous studies. This is the result of empirically confirming the specificity of the COVID-19 period and the negative effects of the integrated economy, and can be used as basic data for effective commercial district management and policy preparation in the event of a national disaster in the future. Third, the characteristics of the background of the commercial district had a significant effect on the sustainability of the commercial district, and the negative effect of the attracting facilities inducing population concentration in the background area was found. This suggests that it is necessary to consider the characteristics of the background as well as the inside of the commercial district when establishing policies to revitalize the commercial district and support small business owners in a national disaster situation.

• Journal of Marine Life Science
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• v.8 no.1
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• pp.8-31
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• 2023

To understand the distribution and formation mechanism of benthic foraminiferal assemblages, grain size analysis, ¹⁴C radiocarbon dating, and benthic foraminifera analysis were conducted on thirty-two surface sediments collected from the continental shelf of the northern East China Sea, respectively. Surface sediment was composed of sandy mud~muddy sand facies with an average of 52.04% of sand, 13.72% of silt, and 34.20% of clay. These sedimentary facies are palimpsest sediment. Benthic foraminifera was classified into a total of 48 genera and 104 species, including agglutinated foraminifera, calcareous-hyaline, and calcareous-porcelaneous foraminifera. The production rate of agglutinated foraminifera increased toward the Yangtze River area while that of planktonic foraminifera increased toward Jeju Island. Dominant species are Ammonia ketienziensis, Bolivina robusta, Eggella advena, Eilohedra nipponica, Pseudorotalia gamardii, Pseudoparrella naraensis. ¹⁴C radiocarbon datings of Bolivina robusta and Pseudorotalia gamardii with the highest production rate were 2,360±40 yr B.P. and 2,450±40 yr B.P., respectively. In the result of cluster analysis, three assemblages composed of P. gaimardii, B. robusta, and A. ketienziensis-P. naraensis were classified broadly. P. gaimardii assemblage is thought to be formed from about 2.5 yr B.P. at the sea area of the Yangtze River to 50 m in water depth affected by fresh water. B. robusta assemblage is thought to be formed from about 2.4 yr B.P. at the sea area of Jeju Island to 50~100 m affected by offshore water. And then, A. ketienziensisP. naraensis assemblage was formed in the northwest sea area (Central Yellow Sea Mud). These distributions and composition of benthic foraminiferal assemblages formed from about 2.5 yr B.P. in the northern East China Sea are thought to be due to the change of benthic ecology environment that occurred by the sea level increase during the late Holocene.

• Korean Journal of Plant Resources
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• v.37 no.4
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• pp.411-422
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• 2024

Naked oat is particularly vulnerable to cold weather among cereal crops, and frequent lodging due to their long culm length. Also it is difficult to cultivate in double cropping system with rice because of late maturity in Korea. In this study, we charaterized 71 naked oat germplasms including two control varieties with agronomic traits, to select superior resources and use them for the improvement of exsiting varieties. The maturity date was May 26th for 'Joyang' and June 1st for 'Daeyang', and there were 5 resources including IT302003, which was earlier than Daeyang. In the case of culm length, the distribution ranged from 62 to 124 cm, and there were 13 resources, including IT209241, that were shorter than 'Joyang' (84 cm) and 'Daeyang' (83 cm). In correlation analysis of agronomic traits, the highest positive correlation (r=0.89) was observed between days to heading and days to maturity. In addition, culm length and spike length (r=0.72) and days to heading (r=0.50) showed a positive correlation. However, Barley yellow dwarf virus(BYDV) and grain yield (r=-0.30), lodging tolerance and liter weight (r=-0.37) showed a negative correlation. Cluster analysis, resources with similar traits such as liter weight, husk ratio, days to heading, and days to maturing were subclassified. Through this analysis, 6 resources, including IT302002 (X345-1-B4-20-1), which had short culm length, early maturity, and cold tolerance compared to 'Joyang' and 'Daeyang', were selected. Among the selected excellent resources, the avenanthramide content of the four resources was IT302002 (325.18 ㎍/g), K000010 (557.3㎍/g), K253299 (447.33 ㎍/g), and K253301 (440.49 ㎍/g), respectively, which was higher than 'Daeyang' (301.97 ㎍/g). These excellent resources will be used as breeding materials for the development of new varieties in the future.

• Journal of the Korean Society of Food Science and Nutrition
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• v.39 no.12
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• pp.1880-1892
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• 2010

The purposes of this study were to: a) provide evidence concerning the effects of emotional intelligence on job outcomes, b) examine the impacts of emotional intelligence on employee-related variables such as 'job satisfaction', 'organizational commitment', 'organizational performance', and 'turnover intention' c) identify the conceptual framework underlying emotional intelligence. A survey was conducted to collect data from foodservice managers (N=231). Statistical analyses were completed using SPSS Win (16.0) for descriptive analysis, reliability analysis, factor analysis, t-test, correlation analysis, cluster analysis and AMOS (16.0) for confirmatory factor analysis and structural equation modeling. The concept of emotional intelligence (EI) has been on the radar screens of many leaders and managers over the last several decades. The emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thinking and interaction with others. The main results of this study were as follows. The four EI (Emotional Intelligence) dimensions correlated significantly with age. The means of job satisfaction score were above the midpoint (3.04 point) scale. The organizational commitment score was above the midpoint (3.41 point) scale and was higher at 'loyalty' factor than 'commitment' factor. The means of organizational performance score were above the midpoint (3.34) scale. The correlations among the four EI (emotional intelligence) factors were significant with job satisfaction; organizational commitment, organizational performance and turnover intention. The test of hypothesis using structural equation modeling found that emotional intelligence produced positive effects on job attitude and job performance. Emotional intelligence enhanced organizational commitment, and in turn, managers' attitude produced positive effects on organizational performance; emotional intelligence also had a direct impact on organizational performance. This study has identified the effect of emotional intelligence on organizational performance and attitudes toward one's job.

• Journal of Distribution Research
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• v.17 no.3
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• pp.25-57
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• 2012

This study identified motives for writing apparel product reviews in online stores, and determined what motives increase the behavior of writing reviews. It also classified store customers based on the type of writing motives, and clarified the characteristics of internet purchase behavior and of a demographic profile. Data were collected from 252 females aged 20s' and 30s' who have experience of reading and writing reviews on online shopping. The five types of writing motives were altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and the expression of satisfaction feelings. Among five motives, altruistic information sharing, economic incentives, and helping new product development stimulate writing reviews. Store customers who write reviews were classified into three groups based on their writing motive types: Other consumer advocates(29.8%), self-interested shoppers(40.5%) and shoppers with moderate motives(29.8%). There were significant differences among three groups in writing behavior (the frequency of writing reviews, writing intent of reviews, duration of writing reviews, and frequency of online shopping) and age. Based on results, managerial implications were suggested. Long Abstract : The purpose of present study is to identify the types of writing motives on online shopping, and to clarify the motives affecting the behavior of writing reviews. This study also classifies online shoppers based on the motive types, and identifies the characteristics of the classified groups in terms of writing behavior, frequency of online shopping, and demographics. Use and Gratification Theory was adopted in this study. Qualitative research (focus group interview) and quantitative research were used. Korean women(20 to 39 years old) who reported experience with purchasing clothing online, and reading and writing reviews were selected as samples(n=252). Most of the respondents were relatively young (20-34yrs., 86.1%,), single (61.1%), employed(61.1%) and residents living in big cities(50.9%). About 69.8% of respondents read and 40.5% write apparel reviews frequently or very frequently. 24.6% of the respondents indicated an "average" in their writing frequency. Based on the qualitative result of focus group interviews and previous studies on motives for online community activities, measurement items of motives for writing after-purchase reviews were developed. All items were used a five-point Likert scale with endpoints 1 (strongly disagree) and 5 (strongly agree). The degree of writing behavior was measured by items concerning experience of writing reviews, frequency of writing reviews, amount of writing reviews, and intention of writing reviews. A five-point scale(strongly disagree-strongly agree) was employed. SPSS 18.0 was used for exploratory factor analysis, K-means cluster analysis, one-way ANOVA(Scheffe test) and ${\chi}^2$-test. Confirmatory factor analysis and path model analysis were conducted by AMOS 18.0. By conducting principal components factor analysis (varimax rotation, extracting factors with eigenvalues above 1.0) on the measurement items, five factors were identified: Altruistic information sharing, remedying of a grievance and vengeance, economic incentives, helping new product development, and expression of satisfaction feelings(see Table 1). The measurement model including these final items was analyzed by confirmatory factor analysis. The measurement model had good fit indices(GFI=.918, AGFI=.884, RMR=.070, RMSEA=.054, TLI=.941) except for the probability value associated with the ${\chi}^2$ test(${\chi}^2$=189.078, df=109, p=.00). Convergent validities of all variables were confirmed using composite reliability. All SMC values were found to be lower than AVEs confirming discriminant validity. The path model's goodness-of-fit was greater than the recommended limits based on several indices(GFI=.905, AGFI=.872, RMR=.070, RMSEA=.052, TLI=.935; ${\chi}^2$=260.433, df=155, p=.00). Table 2 shows that motives of altruistic information sharing, economic incentives and helping new product development significantly increased the degree of writing product reviews of online shopping. In particular, the effect of altruistic information sharing and pursuit of economic incentives on the behavior of writing reviews were larger than the effect of helping new product development. As shown in table 3, online store shoppers were classified into three groups: Other consumer advocates (29.8%), self-interested shoppers (40.5%), and moderate shoppers (29.8%). There were significant differences among the three groups in the degree of writing reviews (experience of writing reviews, frequency of writing reviews, amount of writing reviews, intention of writing reviews, and duration of writing reviews, frequency of online shopping) and age. For five aspects of writing behavior, the group of other consumer advocates who is mainly comprised of 20s had higher scores than the other two groups. There were not any significant differences between self-interested group and moderate group regarding writing behavior and demographics.

• Journal of Nutrition and Health
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• v.52 no.1
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• pp.90-103
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• 2019

Purpose: This study examined the relationship of the nutrition of rice and the positive evaluation of the rice-based meal with the food consumption habits, physical and emotional health status, and learning efficacy of 601 middle and high school students in Jeonju area. Methods: The participants were divided into two groups using cluster analysis in that the participants belonging to the upper groups had a center score of 46.86 (n = 348), while the people belonging to the lower group had a center score of 36.89 (n = 253). Statistical differences were tested for all the relationships between the physical and emotional health symptoms and learning efficacy between the groups at the ${\alpha}=0.05$ level. Results: Significant differences in the physical self-evaluated symptoms were observed in all five items in each cluster (p < 0.05). In the case of the emotional health status, nine out of 10 items showed significant differences between the groups. Similarly, significant differences in all five items in learning efficacy questionnaire were noted (p < 0.05). Positive attitudes of the parents toward having breakfast also showed significant differences among the groups. Conclusion: The nutrition of rice and a positive evaluation of the rice-based meals significantly affect the physical and emotional health status and learning efficacy of juveniles. These findings can be used as baseline information for promoting nutrition education, particularly rice-based breakfast.

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