• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.4
• /
• pp.391-400
• /
• 2019

This study was conducted to investigate the connections between life stress and smartphone addiction of nursing college students. The subjects included nursing college students in some areas. Data were collected in November and December, 2018 from a total of 240 subjects. Collected data were subjected to frequency, percentage, t-test, ${\chi}^2$-test, and ANOVA analyses to identify differences in smartphone addiction level and stress characteristics according to general characteristics. Correlations between smartphone addiction and stress characteristics were investigated by Pearson's correlation analysis, and factors influencing smartphone addiction were examined by hierarchical multiple regression analysis. The findings showed that independent variables had explanatory powers of 14.8% and 32.7% in Models 1 and 2, respectively. The study examined differences in smartphone addiction level according to the general characteristics of the subjects and found that female college students had a higher level of smartphone addiction than their male counterparts. The smartphone addiction level was high in those who were not satisfied with college life, used a smartphone for five hours or more a day, and spent many hours on SNS. Evaluation of differences in stress characteristics according to their general characteristics revealed female college students scored higher for stress characteristics. The means of the stress characteristics were also high for those who were not satisfied with college life, used a smartphone for many hours, and had a high risk of smartphone addiction. In conclusion, female gender, hours of smartphone usage and SNS, academic stress, and value stress were important factors influencing the smartphone addiction of nursing college students. These findings indicate the need to reinforce a stress management program for nursing college students and thus provide them with multifaceted support for stress management.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
• /
• v.17 no.1
• /
• pp.1-13
• /
• 2019

The accident at the Fukushima Dai-ichi Nuclear Power Plant (FDNPP) resulted in the deposition of large quantities of radionuclides over parts of eastern Japan. Radioactive contaminants have been observed over a large area including forests, cities, rivers and lakes. Due to the strong adsorption of radioactive cesium by soil particles, radioactive cesium migrates with the eroded soil, follows the surface flow paths, and is delivered downstream of population-rich regions and eventually to coastal areas. In this study, we developed a model to simulate the transport of contaminated sediment in a watershed hydrological system and this model was compared with observation data from eroded soil observation instruments located at the Korea Atomic Energy Research Institute. Two methods were applied to analyze the soil particle size distribution of the collected soil samples, including standardized sieve analysis and image analysis methods. Numerical models were developed to simulate the movement of soil along with actual rainfall considering initial saturation, rainfall infiltration, multilayer and rain splash. In the 2019 study, a numerical model will be used to add rainfall shield effect by trees, evaporation effect and shield effects of surface water. An eroded soil observation instrument has been installed near the Wolsong nuclear power plant since 2018 and observation data are being continuously collected. Based on these observations data, we will develop the numerical model to analyze long-term behavior of radionuclides on land as they move from land to rivers, lakes and coastal areas.

• Journal of Aerospace System Engineering
• /
• v.13 no.2
• /
• pp.34-42
• /
• 2019

The existence of different thermodynamic properties results in various undesirable effects, such as thermal deformation and residual stress, in heat-welding processes. The solid-state junction, by using explosive or electromagnetic forces, i.e., high-velocity impact welding without employing heat is advantageous in joining materials with different thermodynamic properties. In the solid-state junction, the joining is performed within a short time, a high velocity and large deformations are accompanied by interfacial surfaces. The numerical analysis models play an important role in the understanding of the mechanism of high-velocity impact welding. However, in the analysis of high velocity and large deformations, the conventional Lagrangian method has low reliability due to the occurrence of entanglements. In this study, high-velocity impact welding between Cu and CP-Ti with different thermodynamic properties was performed using a un-gridded numerical method, SPH (Smoothed Particle Hydrodynamics), and interfacial morphology occurred. As a result of the analysis, the interfacial morphology was confirmed and the compared degree of shape (straight, vortex), period, length, and so on appeared differently depending on the relationship between the parameters (impact angle and speed).

• Journal of Korea Water Resources Association
• /
• v.54 no.3
• /
• pp.145-156
• /
• 2021

Forest fires have frequently occurred around the world, and the damages are increasing. In Korea, most forest fires are initiated by human activities, but climate factors such as temperature, humidity, and wind speed have a great impact on combustion environment of forest fires. In this study, therefore, based on statistics of forest fires in Gyeonggi-do over the past five years, meteorological and hydrological factors (i.e., temperature, humidity, wind speed, precipitation, and drought) were selected in order to quantitatively investigate causal relationships with forest fire. We applied a partial least squares structural equation model (PLS-SEM), which is suitable for analyzing causality and predicting latent variables. The overall results indicated that the measurement and structural models of the PLS-SEM were statistically significant for all evaluation criteria, and meteorological factors such as humidity, temperature, and wind speed affected by amount of -0.42, 0.23 and 0.15 of standardized path coefficient, respectively, on forest fires, whereas hydrological factor such as drought had an effect of 0.23 on forest fires. Therefore, as a practical method, the suggested model can be used for analyzing and evaluating influencing factors of forest fire and also for planning response and preparation of forest fire disasters.

• Journal of Nutrition and Health
• /
• v.54 no.1
• /
• pp.116-128
• /
• 2021

Purpose: The extraction conditions for bioactive components from peanut shells, which is a byproduct of peanut processing, were optimized to enhance the total phenolic content (TPC, Y1), total flavonoid content (TFC, Y2), and 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (RSA, Y3). In addition, this study evaluated the anti-obesity effect of peanut shell extract. Methods: Optimization of ultrasonic-assisted extraction (UAE) was performed using a response surface methodology. The independent variables applied for extraction were time (X1: 5.0-55.0), temperature (X2: 26.0-94.0), and ethanol concentration (X3: 0.0%-99.5%). Quadratic regression models were derived based on the results of 17 experimental sets, and an analysis of the variance was performed to verify its accuracy and precision of the regression equations. Results: When evaluating the effects of independent variables on responses using statistically-based optimization, the independent variable with the most significant effect on the TPC, TFC, and RSA was the ethanol concentration (p = 0.0008). The optimal extraction conditions to satisfy all three responses were 35.8 minutes, 82.7℃, and 96.0% ethanol. Under these conditions, the inhibitory activities of α-glucosidase and pancreatic lipase by the extract were 86.4% and 78.5%, respectively. Conclusion: In this study, UAE showed superior extraction efficiency compared to conventional hot-water extraction in the extraction of polyphenols and bioactive materials. In addition, α-glucosidase and pancreatic lipase inhibitory effects were identified, suggesting that peanut shells can be used as effective antioxidants and anti-obesity agents in functional foods and medicines.

• Journal of the Korean Society for information Management
• /
• v.38 no.2
• /
• pp.153-172
• /
• 2021

This study is to create a deep learning based classification model to examine the characteristics of panic disorder and to classify the panic disorder tendency literature by the panic disorder corpus constructed for the present study. For this purpose, 5,884 documents of the panic disorder corpus collected from social media were directly annotated based on the mental disease diagnosis manual and were classified into panic disorder-prone and non-panic-disorder documents. Then, TF-IDF scores were calculated and word co-occurrence analysis was performed to analyze the lexical characteristics of the corpus. In addition, the co-occurrence between the symptom frequency measurement and the annotated symptom was calculated to analyze the characteristics of panic disorder symptoms and the relationship between symptoms. We also conducted the performance evaluation for a deep learning based classification model. Three pre-trained models, BERT multi-lingual, KoBERT, and KcBERT, were adopted for classification model, and KcBERT showed the best performance among them. This study demonstrated that it can help early diagnosis and treatment of people suffering from related symptoms by examining the characteristics of panic disorder and expand the field of mental illness research to social media.

• Journal of Korean Society on Water Environment
• /
• v.27 no.4
• /
• pp.425-432
• /
• 2011

To control non-point source pollution at a watershed scale, rainfall-runoff characteristics from forest watersheds should be investigated since the forest is the dominant land use in Korea. Long-term monitoring would be an ideal method. However, computer models have been utilized due to limitations in cost and labor in performing long-term monitoring at the watersheds. In this study, the Geo-spatial interface to the Water Erosion Prediction Project (GeoWEPP) model was evaluated for its runoff prediction from a coniferous forest dominant watersheds. The $R^2$ and the NSE for calibrated result comparisons were 0.77 and 0.63, validated result comparisons were 0.92, 0.89, respectively. These comparisons indicated that the GeoWEPP model can be used in evaluating rainfall-runoff characteristics. To estimate runoff changes from a coniferous forest watershed with various cover degree scenarios, ten cover degree scenarios (10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%) were run using the calibrated GeoWEPP model. It was found that runoff increases with decrease in cover degree. Runoff volume was the highest ($206,218.66m^3$) at 10% cover degree, whereas the lowest ($134,074.58m^3$) at 100% cover degree due to changes in evapotranspiration under various cover degrees at the forest. As shown in this study, GeoWEPP model could be efficiently used to investigate runoff characteristics from the coniferous forest watershed and effects of various cover degree scenarios on runoff generation.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.6
• /
• pp.1089-1098
• /
• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Korean Geotechnical Society
• /
• v.36 no.12
• /
• pp.77-86
• /
• 2020

In this study, the importance of implied strength correction for shallow depths at a region of moderate to low seismicity with primary focus on its effect upon site natural period and mean period of the ground motion is investigated. In addition to the most commonly used Modified Kondner-Zelasko (MKZ) model, this paper uses a quadratic/hyperbolic (GQ/H) model that can capture the stress - strain response at large strains as well as small strain stiffness dependence. A total of six site profiles by downhole tests are used and 1D site response analyses are performed using three input motions with contrasting mean periods. The difference between non-corrected and corrected analyses is conditional on the site period as well as mean ground motion period. The effect of periods is analyzed by correlating them with the effective peak ground acceleration, maximum shear strains and amplification factors. The comparative study reveals that the difference is more prominent in soft sites with long site periods. Insignificant differences are observed when soil profiles are subjected to ground motion with very short mean period.

• Journal of The Korean Society of Grassland and Forage Science
• /
• v.41 no.1
• /
• pp.29-34
• /
• 2021

Near infrared reflectance spectroscopy (NIRS) is routinely used for the determination of nutrient components of forages. However, little is known about the impact of sample preparation and wavelength on the accuracy of the calibration to predict minerals. This study was conducted to assess the effect of sample preparation and wavelength of near infrared spectrum for the improvement of calibration and prediction accuracy of Calcium (Ca) and Phosphorus (P) in imported hay using NIRS. The samples were scanned in reflectance in a monochromator instrument (680-2,500 nm). Calibration models (n = 126) were developed using partial least squares regression (PLS) based on cross-validation. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2) and the lowest standard error of cross-validation (SECV). The highest R2 and the lowest SECV were obtained using oven-dry grinded sample preparation and 1,100-2,500 nm wavelength. The calibration (R2) and SECV were 0.99 (SECV: 468.6) for Ca and 0.91 (SECV: 224.7) for P in mg/kg DM on a dry weight, respectively. Results of this experiment showed the possibility of NIRS method to predict mineral (Ca and P) concentration of imported hay in Korea for routine analysis method to evaluate the feed value.