• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.1
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• pp.125-137
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• 2019

This study determined the impact of 'Fashion Augmented Reality (FAR)' acceptance factors based on the model of acceptance and use of technology (UTAUT) on consumer attitudes, intention to use technology, and fashion product purchase intention. A survey asked participants to have an AR experience using a FAR app to understand FAR in advance. Data were analyzed factor analysis and stepwise regression using SPSS. The results are as follows. First, the factor analysis classified the acceptance variables of FAR technology into 'social relations', 'shopping effectiveness', and 'easy to use FAR'. Second, among the three factors of FAR acceptance, 'shopping effectiveness' is statistically more influential on positive attitudes towards FAR. However, 'easy to use' factor was more influential on 'the intention to use technology' as well as 'purchase intention'. Third, 'social relations' were identified as an important factor affecting 'consumer attitudes', 'intention to use technology' and 'purchase intention' which are not well covered in fashion technology research. In addition, 'the intention to use technology' was found to be influential on 'purchase intention' and indicated the importance of easiness of FAR to enhance purchase intention.

• The Journal of Korean Academic Society of Nursing Education
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• v.27 no.4
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• pp.412-422
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• 2021

Purpose: The purpose of this study was to analyze the factors influencing the health promoting behavior(s) of office worker males in the COVID-19 pandemic by applying Pender's health promotion model. Methods: The participants in this study were 149 male office workers at companies located in S, G and S cities. The collected data were analyzed using descriptive statistics, t-test, ANOVA, Pearson's correlation coefficient and a stepwise multiple regression using the SPSS Window 25.0 program. Results: The subject's health promoting behaviors and prior related behaviors (r=.58, p<.001), perceived benefits of action (r=.41, p<.001), self-efficacy (r=.53, p<.001), social support (r=.39, p<.001), self-esteem (r=.47, p<.001) and commitment to a plan of action (r=.67, p<.001) showed a high positive correlation. The factors affecting the subjects' health promoting behaviors were the commitment to a plan of action (𝛽=.35, p<.001), self-esteem (𝛽=.27, p=.005), prior related behavior (𝛽=.26, p<.001), health status (good) (𝛽=.20, p=.001) and self-efficacy (𝛽=.14, p=.047). These variables explained 63.0% of the subjects' health promoting behaviors. Conclusion: During the COVID-19 pandemic, subjects are more likely to be exposed to disease due to reduced outdoor activity time and irregular eating habits due to the strengthening of social distancing. Health promoting behaviors are an important concept that can maintain health and prevent diseases. To improve the health promoting behaviors of men engaged in office work, it is necessary to develop and operate a health promotion behaviors program considering those variables.

• Journal of the Korean Society of Food Culture
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• v.23 no.6
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• pp.779-792
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• 2008

The consumption of a wide variety of food groups is considered one of the key components of nutritional adequacy. The 2005 Dietary Guidelines for Koreans includes the consumption of a variety of foods from diverse food groups as a component of a normal diet. A survey was conducted to evaluate the relationship between the dietary diversity of food groups and nutrient intake in elderly patients (age 65 and above) at a rural area in Korea (Inje). 296 subjects (111 male and 185 female) were probed in a 3 day 24-recall dietary survey. Subjects were grouped according to food group intake frequency, based on six food groups (grain, meat/fish/legume/egg, vegetable, fat/oil, dairy, fruit). Nutritional quality was evaluated according to the numbers of nutrients under EAR (Estimated Average Requirements), and MAR (mean adequacy ratios). The frequency of elderly subjects consuming a meat/fish/legumes/egg food group less than once per day was 29.4%. The frequency of elderly subjects consuming fat/oil food group less than once per day was 65.8%. The percentage of subjects who did not eat dairy food was 88.8%, and that of subjects who did not eat fruit was 57.5%. A stepwise multiple regression analysis was used to develop models relating nutritional quality to possible food group intake frequency factors. Using the number of nutrients under EAR as a dependent variable, the meat/fish/legume/egg food group intake frequency explained 9.9% of variance, followed by the grain group, fat/oil group, dairy group, and vegetable and fruit group (Model $R^2$=0.260). For mean nutrient adequacy ratio as a dependent variable, the model $R^2$ was 0.326. The results of this study suggest that a highly varied diet in elderly might be associated with better nutritional quality, as assessed by nutrient intake. Accordingly, dietary guidelines should take into consideration nutritional characteristics in order to improve intake from all major food groups and to provide a variety of foods in the diet.

• Geomechanics and Engineering
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• v.37 no.4
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• pp.307-321
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• 2024

This paper delves into the critical assessment of predicting sidewall displacement in underground caverns through the application of nine distinct machine learning techniques. The accurate prediction of sidewall displacement is essential for ensuring the structural safety and stability of underground caverns, which are prone to various geological challenges. The dataset utilized in this study comprises a total of 310 data points, each containing 13 relevant parameters extracted from 10 underground cavern projects located in Iran and other regions. To facilitate a comprehensive evaluation, the dataset is evenly divided into training and testing subset. The study employs a diverse array of machine learning models, including recurrent neural network, back-propagation neural network, K-nearest neighbors, normalized and ordinary radial basis function, support vector machine, weight estimation, feed-forward stepwise regression, and fuzzy inference system. These models are leveraged to develop predictive models that can accurately forecast sidewall displacement in underground caverns. The training phase involves utilizing 80% of the dataset (248 data points) to train the models, while the remaining 20% (62 data points) are used for testing and validation purposes. The findings of the study highlight the back-propagation neural network (BPNN) model as the most effective in providing accurate predictions. The BPNN model demonstrates a remarkably high correlation coefficient (R2 = 0.99) and a low error rate (RMSE = 4.27E-05), indicating its superior performance in predicting sidewall displacement in underground caverns. This research contributes valuable insights into the application of machine learning techniques for enhancing the safety and stability of underground structures.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.11
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• pp.1537-1544
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• 2015

Meat and carcass quality attributes are of crucial importance influencing consumer preference and profitability in the pork industry. A set of 400 Berkshire pigs were collected from Dasan breeding farm, Namwon, Chonbuk province, Korea that were born between 2012 and 2013. To perform genome wide association studies (GWAS), eleven meat and carcass quality traits were considered, including carcass weight, backfat thickness, pH value after 24 hours (pH24), Commission Internationale de l'Eclairage lightness in meat color (CIE L), redness in meat color (CIE a), yellowness in meat color (CIE b), filtering, drip loss, heat loss, shear force and marbling score. All of the 400 animals were genotyped with the Porcine 62K SNP BeadChips (Illumina Inc., USA). A SAS general linear model procedure (SAS version 9.2) was used to pre-adjust the animal phenotypes before GWAS with sire and sex effects as fixed effects and slaughter age as a covariate. After fitting the fixed and covariate factors in the model, the residuals of the phenotype regressed on additive effects of each single nucleotide polymorphism (SNP) under a linear regression model (PLINK version 1.07). The significant SNPs after permutation testing at a chromosome-wise level were subjected to stepwise regression analysis to determine the best set of SNP markers. A total of 55 significant (p<0.05) SNPs or quantitative trait loci (QTL) were detected on various chromosomes. The QTLs explained from 5.06% to 8.28% of the total phenotypic variation of the traits. Some QTLs with pleiotropic effect were also identified. A pair of significant QTL for pH24 was also found to affect both CIE L and drip loss percentage. The significant QTL after characterization of the functional candidate genes on the QTL or around the QTL region may be effectively and efficiently used in marker assisted selection to achieve enhanced genetic improvement of the trait considered.

• Korean Journal of Psychosomatic Medicine
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• v.21 no.1
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• pp.18-26
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• 2013

Objectives : Type D personality was originally introduced to study the role of personality in predicting outcomes of heart disease. However, researches showed that other medical conditions are also affected by this personality. The purpose of this study was to evaluate the relationship between type D personality and somatic symptom complaints in depressive patients. Methods : Eighty-two individuals diagnosed with depressive disorder were included. Type D personality was measured with DS14. Patient Health Questionnaire(PHQ) 9 and 15 were used to measure depression severity and somatization tendencies. For alexithymia, TAS-20 was used. Student T-test and linear regression analysis were performed. The best regression model was determined by stepwise variable selection. Results : More than half of the subjects(56%) complained at least medium degree somatic symptoms according to PHQ-15 criteria. Two-thirds of the subjects were classified as Type D personality(63.4%). The mean PHQ-15 score of the Type D individuals was significantly higher than the remaining subjects(PHQ-15 mean=12.7, $p=8.2{\times}10^{-7}$). The best regression model included age, PHQ-9 score and NA subscale score as predictor variables. Among these, only the coefficients of age($p=1.5{\times}10^{-3}$) and NA score($p=1.5{\times}10^{-7}$) were found to be statistically significant. Conclusions : The result showed that Type D personality was one of the strong predictors of somatic complaints among depressive individuals. The finding that negative affectivity rather than social inhibition was more closely associated with somatization tendencies does not fully agree with the traditional explanation that inability to express negative emotion predispose the individuals to somatic symptoms. The finding that alexithymia was not shown to be a significant predictors also substantiated this discrepancy. However, it might be possible that the high correlation between NA and SI subscore(r=0.65) and between NA and TAS-20 score(r=0.44) hid the additional effects of social inhibition and alexithymia. Further research with a larger sample would be needed to investigate the effects of the latter two components over and above the effect of negative affectivity on the somatic complaints in depressive patients.

• The Journal of the Korea Contents Association
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• v.12 no.11
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• pp.338-357
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• 2012

This study investigated any possible financial attributes of the CDS spreads of a firm belonging to financial industries headquartered in the Republic of Korea. There were few studies on this issue, especially for the firms located in emerging capital markets. Coupled with the models such as a multiple regression and a principal component analysis(PCA), this research has identified that only two explanatory variables such as SLOPE and INTER3 (i.e. interaction effect between the BETA and the SLOPE) consistently showed their statistically significant influence on the CDS spreads through the 'selected' model without and with applying a stepwise regression procedure for the robustness. Given the rapid developments of sophisticated financial derivatives, this study may suggest a valuable insight to foreign and domestic investors to identify the possible determinants of CDS spreads at the firm- and/or the industry-level.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.637-648
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• 2017

The purpose of this study was to investigate the relationships among smartphone addiction, communication ability, loneliness, and interpersonal relationships in university students in Korea. Between 15 and 31 May 2016, 263 subjects completed a questionnaire consisting of questions on smartphone addiction, communication ability, loneliness, and health of interpersonal relationships. Among smartphone addiction questions there were significant differences between genders (t = 2.350, p = 0.019) and educational grade (F = 3.313, p = 0.021). With regard to human relationship health, the scores for males were significantly higher than those for females (t = 2.337, p = 0.020). The smartphone addiction and human relationship health results were negatively correlated (r = -0.157, p = 0.011). In contrast, communication ability and human relationship health results were positively correlated (r = 0.581, p < 0.001). However, loneliness and human relationship health were not significantly correlated. The final multiple regression model explaining human relationship health included smartphone addiction (t = 2.753, p = 0.006), communication ability (t = 11.714, p < 0.001), and motivation for smartphone purchase (t = 2.125, p = 0.035) as independent factors. The final model explained 36.6% of the total variance. In order to improve university students' human relationship health, solutions for smartphone addiction and low communication skills are required. This results of this study can be used as a basis on which to develop a human relationship health intervention program.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.9
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• pp.1215-1221
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• 2016

The prediction of carcass composition in Hanwoo steers is very important for value-based marketing, and the improvement of prediction accuracy and precision can be achieved through the analyses of independent variables using a prediction equation with a sufficient dataset. The present study was conducted to develop a prediction equation for Hanwoo carcass composition for which data was collected from 7,907 Hanwoo steers raised at a private farm in Gangwon Province, South Korea, and slaughtered in the period between January 2009 and September 2014. Carcass traits such as carcass weight (CWT), back fat thickness (BFT), eye-muscle area (EMA), and marbling score (MAR) were used as independent variables for the development of a prediction equation for carcass composition, such as retail cut weight and percentage (RC, and %RC, respectively), trimmed fat weight and percentage (FAT, and %FAT, respectively), and separated bone weight and percentage (BONE, and %BONE), and its feasibility for practical use was evaluated using the estimated retail yield percentage (ELP) currently used in Korea. The equations were functions of all the variables, and the significance was estimated via stepwise regression analyses. Further, the model equations were verified by means of the residual standard deviation and the coefficient of determination ($R^2$) between the predicted and observed values. As the results of stepwise analyses, CWT was the most important single variable in the equation for RC and FAT, and BFT was the most important variable for the equation of %RC and %FAT. The precision and accuracy of three variable equation consisting CWT, BFT, and EMA were very similar to those of four variable equation that included all for independent variables (CWT, BFT, EMA, and MAR) in RC and FAT, while the three variable equations provided a more accurate prediction for %RC. Consequently, the three-variable equation might be more appropriate for practical use than the four-variable equation based on its easy and cost-effective measurement. However, a relatively high average difference for the ELP in absolute value implies a revision of the official equation may be required, although the current official equation for predicting RC with three variables is still valid.

• The Journal of the Korea Contents Association
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• v.22 no.10
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• pp.488-496
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• 2022

For over two years, after COVID-19 was first reported in the last quarter of 2019, the world has suffered from the pandemic. The Korean government has taken an initiative and has implemented a variety of policies to protect people from COVID-19. These policies have resulted in some suffering and inconvenience for people. In this context, we aim to find out what factors influence Korean college students' intention to social distance. We surveyed with Google's online survey tool(Google Form) for 116 Korean college students using a convenient sampling from December 30, 2020, to January 8, 2021. We put perceived susceptibility, perceived severity, perceived health status, level of involvement, and trust in the policy as independent variables into a multiple regression equation using the stepwise method. We found that intention to social distance was predicted by perceived susceptibility, level of involvement, and trust in the policy in the final model. The findings mean that the more people perceive themselves susceptible to COVID-19, and the more they get involved with COVID-19, and the more they trust their governmental policies on COVID-19, the more they agree on social distancing.