• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.449-457
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• 2023

The purpose of this descriptive research study was to investigate health beliefs and self-efficacy in respiratory infection management as factors that affect the respiratory infection prevention behavior of college students. The subjects were 178 students attending a university in K city of Gyeongsangbuk-do. Data were collected with a structured questionnaire from September 1st to October 16th of 2020. The results of this study are as follows; Health belief was significantly different from participant's gender (t=-2.86, p=.005), major classification (F=2.95, p=.034), and taking any medications (t=2.18, p=.030). Self-efficacy in respiratory infection management was significantly different from university students' gender (t=-3.56, p=<.001) and major classification (F=4.59, p=.004). Health belief (r=.276, p<.001) and self-efficacy in respiratory infection management (r=.660, p<.001) had a positive correlation with respiratory infection preventive behavior. Multiple regression analysis results show that self-efficacy in respiratory infection management (β=.66, p<.001) significantly affected respiratory infection preventive behavior. The model had an explanatory power of 43%. The findings demonstrate that the major factor influencing the respiratory infection preventive behavior of university students is self-efficacy in respiratory infection management. Therefore, in order to promote behavior to prevent respiratory infection in college students, a program that can strengthen self-efficacy in respiratory infection management should be developed.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.79-88
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• 2023

Objectives : Cancer diagnosis causes significant distress while it may also bring positive change: post-traumatic growth. This study was conducted to analyze factors that affect post-traumatic growth. Methods : Medical records of 52 cancer patients who received psychiatric treatment at a university hospital in Seoul were reviewed and the correlation between post-traumatic growth and following factors were analyzed: Resilience, Anxious thoughts and tendencies, Mindful attention awareness, Acceptance attitude Results : Using Multiple Generalized Linear model, a positive correlation was found between post-traumatic growth and resilience (B=1.45, p<0.0001), mindful attention awareness (B=0.58, p=0.0030) and acceptance attitude (B=1.29, p=0.0003), while anxious thoughts and tendencies (B=-0.84, p<0.0001) had negative association. Conclusions : Factors that have a positive impact on post-traumatic growth were resilience, mindful attention awareness, acceptance attitude and a factor with a negative impact was anxious thoughts and tendencies; Factors that impact post-traumatic growth need to be taken into account, when approaching the treatment of cancer patients.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.473-487
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• 2023

Korea's multi-purpose dams, which were constructed in the 1970s and 1980s, have a single outlet located near the bottom for hydropower generation. Problems such as freezing damage to crops due to cold water discharge and an increase the foggy days have been raised downstream of some dams. In this study, we analyzed the effect of water intake depth on the reservoir's water temperature stratification structure and outflow temperature targeting Hapcheon Reservoir, where hypolimnetic withdrawal is drawn via a fixed depth outlet. Using AEM3D, a three-dimensional hydrodynamic water quality model, the vertical water temperature distribution of Hapcheon Reservoir was reproduced and the seasonal water temperature stratification structure was analyzed. Simulation periods were wet and dry year to compare and analyze changes in water temperature stratification according to hydrological conditions. In addition, by applying the intake depth change scenario, the effect of water intake depth on the thermal structure was analyzed. As a result of the simulation, it was analyzed that if the hypolimnetic withdrawal is changed to epilimnetic withdrawal, the formation location of the thermocline will decrease by 6.5 m in the wet year and 6.8 m in the dry year, resulting in a shallower water depth. Additionally, the water stability indices, Schmidt Stability Index (SSI) and Buoyancy frequency (N²), were found to increase, resulting in an increase in thermal stratification strength. Changing higher withdrawal elevations, the annual average discharge water temperature increases by 3.5℃ in the wet year and by 5.0℃ in the dry year, which reduces the influence of the downstream river. However, the volume of the low-water temperature layer and the strength of the water temperature stratification within the lake increase, so the water intake depth is a major factor in dam operation for future water quality management.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.693-699
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• 2023

Ultrasound is widely used in the medical field for non-destructive and non-invasive disease diagnosis. In order to improve the disease diagnosis accuracy of diagnostic medical images, improving spatial resolution is a very important factor. In this study, we aim to model the super resolution convolutional neural network (SRCNN) algorithm in ultrasound images and analyze its applicability in the medical diagnostic field. The study was conducted as an experimental study using Field II simulation and open source clinical liver hemangioma ultrasound imaging. The proposed SRCNN algorithm was modeled so that end-to-end learning can be applied from low resolution (LR) to high resolution. As a result of the simulation, we confirmed that the full width at half maximum in the phantom image using a Field II program was improved by 41.01% compared to LR when SRCNN was used. In addition, the peak to signal to noise ratio (PSNR) and structural similarity index (SSIM) evaluation results showed that SRCNN had the excellent value in both simulated and real liver hemangioma ultrasound images. In conclusion, the applicability of SRCNN to ultrasound images has been proven, and we expected that proposed algorithm can be used in various diagnostic medical fields.

• Genomics & Informatics
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• v.21 no.3
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• pp.33.1-33.11
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• 2023

Type 2 diabetes mellitus (T2DM) is a multifactorial, polygenic, and metabolically complicated disease. A large number of genes are responsible for the biogenesis of T2DM and calpain10 (CAPN10) is one of them. The association of numerous CAPN10 genetic polymorphisms in the development of T2DM has been widely studied in different populations and noticed inconclusive results. The present study is an attempt to evaluate the plausible association of CAPN10 polymorphism SNP-19 (rs3842570) with T2DM and T2DM-related anthropometric and metabolic traits in the Noakhali region of Bangladesh. This case-control study included 202 T2DM patients and 75 healthy individuals from different places in Noakhali. A significant association (p < 0.05) of SNP-19 with T2DM in co-dominant 2R/3R vs. 3R/3R (odds ratio [OR], 2.7; p=0.0014) and dominant (2R/3R) + (2R/2R) vs. 3R/3R (OR, 2.47; p=0.0011) genetic models was observed. High-risk allele 2R also showed a significant association with T2DM in the allelic model (OR, 1.67; p=0.0109). The genotypic frequency of SNP-19 variants showed consistency with Hardy-Weinberg equilibrium (p > 0.05). Additionally, SNP-19 genetic variants showed potential associations with the anthropometric and metabolic traits of T2DM patients in terms of body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol, and triglycerides. Our approach identifies the 2R/3R genotype of SNP-19 as a significant risk factor for biogenesis of T2DM in the Noakhali population. Furthermore, a large-scale study could be instrumental to correlate this finding in overall Bangladeshi population.

• Journal of the Korean Applied Science and Technology
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• v.39 no.6
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• pp.727-737
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• 2022

This study was conducted to identify the factors Influencing the burnout of general hospital nurses. Method: The study subjects were 150 nurses in three general hospital. The collected data were analyzed by t-test, ANOVA, Scheffe's test, Pearson's correlation coefficient, and Multiple regression analysis. Results: The factors affecting the burnout of the subjects, multiple regression analysis results showed that emotional intelligence(β=-.441, p<.001), emotional labor(β=.403, p<.001), current position was more than responsible nurse(β=-.111, p<.018), and health status was healthy(β=-.100, p<.029). In addition, the F statistics for the fitness of the estimated regression model were 35.51(p<.001), which was very significant. The explanatory power was 79.7%. Conclusion: The results of this study showed that emotional intelligence of the general hospital nurse was the most influential factor on burnout, and the higher the position, the better the health status, the lower the emotional labor, the lower the burnout. Therefore, the results of this study suggest that it is necessary to find ways to reduce emotional labor and improve health and emotional intelligence in order to reduce burnout of nurses, and it is considered to be useful as basic data for developing intervention programs to lower burnout.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.434-440
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• 2022

The ICAO (International civil aviation organization)recommended the implementation of the GANP (global air navigation plan) for strategic decision-making and air traffic management evaluation. In this study, we proposed a new method for finding the route distance from KPI (key performance indicator) 05 actual route extension presented for air traffic management evaluation. For this purpose, we collected trajectory data for one month and calculated the en-route distances using the methods presented in ICAO and the methods presented by this author. In the ICAO method, the intersection point must be estimated through the equation of a circle for radius 40 NM and the equation of a straight line for an inner and outer point close to a circle in the track data, and four flight distances are calculated to calculate the en-route distance. In the method presented in this study, two flight distances are calculated without estimating the intersection point to calculate the en-route distance. To determine the error between the two methods, we used the performance evaluation index RMSE (root mean square error) and the determination factor R² of the regression model.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• Journal of the Society of Disaster Information
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• v.19 no.4
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• pp.770-778
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• 2023

Purpose: In order to reduce forklift industrial accidents, it is necessary to classify them into institutional, technical, and educational factors and conduct research on whether each factor affects disaster reduction. Method: Descriptive statistical analysis, validity analysis, reliability analysis, and multiple regression analysis were conducted using SPSS 18 program based on an offline questionnaire based on a 5-point Likert scale. Result: As a result of multiple regression analysis, it was found that institutional, technical, and educational factors, which are independent variables for disaster reduction, explain about 62.5% of the variance in disaster prevention, which is the dependent variable. The regression model verification was found to be statistically significant with F=118.775 and significance probability p<0.01. Conclusion: First, there is a need to prevent disasters by including electric forklifts weighing less than 3 tons in the inspection system. Second, there is a need to make it mandatory to install front and rear cameras and forklift line beams to prevent forklift collision disasters. Third, there is a need to conduct special training related to forklifts every year, and drivers and nearby workers need to be included in the special training for forklifts.

• Journal of Venture Innovation
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• v.4 no.1
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• pp.39-50
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• 2021

This was empirical research aimed at determining the effects of emotional labor, Resilience and service environment, on the performance of Long-term Care Hospital Employee. The participants were 180 employees working in long-term care hospitals in Gyeonggi-do. The collected data were analyzed using the SPSS statics19.0 program. The study were analyzed by frequency analysis and descriptive statistics, ANOVA, Scheffe? test, Pearson correlation coefficients, and stepwise regression. As a result of the study, age, marriage status, career, and position affected performance among the general characteristics.tion coefficients, and stepwise regression. As a result of the study, the average performance was 91.25 (±12.46) points, emotional labor was 41.25 (±4.21) points, Resilience was 52.89 (±6.37), and the service environment was 78.93(±15.3) points. The performance showed a positive correlation with emotional labor(r=.326, p<.001) service environment (r=.384, p=.005) and Resilience (r=.417, p<.001) of Long-term Care Hospital Employee. Service environment was the biggest factor affecting performance, and the second was resilience. The explanatory power of this regression model was 48.2% and was statistically significant (F=58.249, p<.001).