• Journal of the Korean Data Analysis Society
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• v.20 no.6
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• pp.3201-3209
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• 2018

The purpose of this study was to explore the factors predicting increased usage hours of smartphone among adolescents. Secondary data was analyzed to be collected from a nationally representative sample of 2017 Korean adolescents. This study sample included 54,601 students in middle or high schools of Korea. The collected data were analyzed SPSS version 23.0 program for frequency, percentage, mean, standard deviation, t-test, ANOVA, Pearson's correlation coefficient and binary logistic regression analysis. In the results, the mean usage hour of smartphone among the adolescents was 28.42 (SD 23.30) per week. Analyses of the differences in usage hours of smartphone according to research variables were found that the groups of lower level of study (F=1361.067, p<.001) and sociality content type (F=761.549, p<.001) spent more time, as compared to the other groups. The logistic analysis showed the predictive factors for increased hour of using smartphone were smartphone usage for sociality (OR: 2.44, 95% CI: 2.26-2.64) and peer group counselor (OR: 1.49, 95% CI: 1.49). Conclusionally, the findings of this study suggests that it needs to understand cause or purpose of smartphone using of adolescent and to cope and educate on the cause.

• Fisheries and Aquatic Sciences
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• v.26 no.12
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• pp.738-751
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• 2023

The Sembilan Archipelago is famous for its great biodiversity, in which the humphead wrasse (Cheilinus undulatus) (locally named Napoleon fish) is the primary commodity (economically important), and currently, the environmental degradation occurs due to anthropogenic activities. This study aimed to examine the eco-environmental parameters and assess their influence on the abundance of humphead wrasse and other coral reef fish compositions in the Sembilan Archipelago. Direct field monitoring was performed using a visual census throughout an approximately one km transect. Coral cover data collection and assessment were also carried out. A coastal water quality index (CWQI) was used to assess the water quality status. Furthermore, statistical-based analyses [hierarchical clustering, Pearson's correlation, principal component analysis (PCA), and canonical correspondence analysis (CCA)] were performed to examine the correlation between eco-environmental parameters. The Napoleon fish was only found at stations 1 and 2, with a density of about 3.8 Ind/ha, aligning with the dominant composition of the family Serranidae (covering more than 15% of the total community) and coinciding with the higher coral mortality and lower reef fish abundance. The coral reef conditions were generally ideal for supporting marine life, with a living coral percentage of about > 50% in all stations. Based on CWQI, the study area is categorized as good and excellent water quality. Of the 60 parameter values examined, the phytoplankton abundance, Napoleon fish, and temperature are highly correlated, with a correlation coefficient value greater than 0.7, and statistically significant (F < 0.05). Although the adaptation of reef fish to water quality parameters varies greatly, the most influential parameters in shaping their composition in the study area are living corals, nitrites, ammonia, larval abundance, and temperature.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.1
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• pp.83-93
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• 2016

Objectives : The Center for Epidemiologic Studies Depression scale-Revised is a recently revised scale which has been reported as a valid tool for the assessment of depressive symptoms. It encompasses cardinal symptoms of depression described in the Diagnostic and Statistical Manual of Mental disorders, fourth edition. In this study, we assessed the reliability, validity and psychometric properties of the Korean version of the CESD-R(K-CESD-R). Methods : Forty-eight patients diagnosed as major depressive disorder, dysthymia, depressive disorder NOS according to the DSM-IV criteria using Mini International Neuropsychiatric Interview and 48 healthy controls were enrolled in this study. They were assessed with K-CESD-R, K-MADRS, PHQ-9, KQIDS-SR, STAI to check cross-validation. Statistical analyses were performed using calculation of Cronbach's alpha, Pearson correlation coefficient, Principal Component Analysis, ROC curve and optimal cut-off value. Results : The Cronbach's alpha of K-CESD-R was 0.98. The total score of K-CESD-R revealed significantly high correlations with those of K-MADRS, PHQ-9, KQIDS-SR(r=0.910, 0.966 and 0.920, p<0.001, respectively). Factor analysis showed two factors account for 76.29% of total variance. We suggested the optimal cut-off value of K-CESD-R as 13 according to analysis of the ROC curve which value sensitivity and specificity both equally. Conclusions : These Results showed that the K-CESD-R could be a reliable and valid scale to assess depressive symptoms. The K-CESD-R is expected as a useful and effective tool for screening and measuring depressive symptoms not only in outpatient clinic but also epidemiologic studies.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1544-1557
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• 2020

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (r_M1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (r_M2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The r_M1 estimates in the study were between 0.90 and 0.96 among five traits. The r_M1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average r_M2 estimates were much smaller than r_M1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, r_M2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of r_M2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the r_M2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based r_M2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.10
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• pp.1488-1496
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• 2016

The purposes of this study were to analyze mothers' eating, cooking, and exercise habits based on their demographic characteristics and to examine the relationship between those habits and their late school-aged children's Broca index. A total of 393 questionnaires were mailed to the mothers of late school-aged children who registered at four elementary schools in the Seoul area, of which 159 participants (40.0%) completed questionnaires. Statistical data analyses were performed using SPSS/Win 21.0 for descriptive statistics, t-test ANOVA, and Pearson's regression coefficient. There was a statistically significant difference in mothers' cooking habit (F=3.920, P=0.022) and exercise habit (F=3.211, P=0.043) according to their educational level. Interestingly, 82.4% of mothers had a Broca index of less than 90% of normal body mass level. A significant positive correlation of Broca index between mothers and their late school-aged children (r=0.345, P<0.001) indicated that children whose mothers had a low body mass level also tended to have a low body mass level. In this study, late school-aged children's Broca index was not significantly related with mother's eating (r=-0.072, P=0.367) or exercise habits (r=-0.010, P=0.897) but was significantly related with their mother's cooking habits (r=-0.157, P=0.048). Considering there are few studies examining the impacts of mother's cooking habits on their children's appropriate body mass, the results suggest that developing an effective educational program to cultivate mothers' healthy cooking habits to improve school-aged children's health status is very important. The findings of this study provide important data that could be used when developing health education programs tailored to the multi-dimensional impacts of mothers' life habits on their last school-aged children's developmental health status.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.