• Journal of the Korea Convergence Society
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• v.12 no.6
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• pp.241-248
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• 2021

The purpose of this study was to investigate the factors affecting the rights guarantee of people with mental illness among mental care facility workers. The subjects of this study were 132 mental care facility workers, and the research tools were rights and protection-related characteristics, rights recognition, and rights guarantee. The data were analyzed with descriptive statistics, t-test, one-way ANOVA, Pearson's correlation coefficients, and multiple linear regression using the SPSS/WIN 24.0 program. The result showed that the difference in rights guarantee relating to general characteristics were significant differences in religion. There were significant positive correlations among rights recognition and rights guarantee. The factors influencing the rights guarantee were rights recognition, recognizing the need to advocate rights, and religion. Based on the research results, it is necessary to improve recognition and actively advocate rights through continuous education in order to strengthen the rights guarantee of people with mental illness. Enhancement of rights guarantee will help people with mental disorder recover.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.261-267
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• 2021

The mission of the reserve forces unit is to prepare good training for reserve forces during peacetime. For good training, units require proper organization support agents, but they have difficulties due to a lack of unit members. For that reason, the units forecast the monthly attendance rate of reserve forces (using the x-1 year's result) to organize support agents and unit schedule. On the other hand, the existing planning method can have more errors compared to the actual result of the attendance rate. This problem has a negative effect on the training performance. Therefore, it requires more accurate forecast models to reduce attendance rate errors. This paper proposes an attendance rate forecast model using data mining. To verify the proposed data mining based model, the existing planning method was compared with the proposed model using real data. The results showed that the proposed model outperforms the existing planning method.

• Advances in environmental research
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• v.10 no.1
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• pp.17-41
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• 2021

Kazakhstan's cities experience high concentrations levels of atmospheric particulate matter (PM), which is well-known for its highly detrimental effect on the human health. A further increase in PM concentrations in the future could lead to a higher air pollution-caused morbidity and mortality, causing an increase in healthcare expenditures by the government. However, to prevent elevated PM concentrations in the future, more stringent standards could be implemented by lowering current maximum allowable PM concentration limit to Organization for Economic Co-operation and Development (OECD)'s limits. Therefore, this study aims to find out what impact this change in environmental policy towards PM has on state economy in the long run. Future PM10 and PM2.5 concentrations were estimated using multiple linear regression based on gross regional product (GRP) and population growth parameters. Dose-response model was based on World Health Organization's approach for the identification of mortality, morbidity and healthcare costs due to air pollution. Analysis of concentrations revealed that only 6 out of 21 cities of Kazakhstan did not exceed the EU limit on PM10 concentration. Changing environmental standards resulted in the 71.7% decrease in mortality and 77% decrease in morbidity cases in all cities compared to the case without changes in environmental policy. Moreover, the cost of morbidity and mortality associated with air pollution decreased by $669 million in 2030 and $2183 million in 2050 in case of implementation of OECD standards. Thus, changing environmental regulations will be beneficial in terms of both of mortality reduction and state budget saving.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.183-201
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• 2022

Estimating the implicit value of housing assets is a very important task for participants in the housing market. Until now, such estimations were usually carried out using multiple regression analysis based on the inherent characteristics of the estate. However, in this paper, we examine the estimation capabilities of the Artificial Neural Network(ANN) and its 'Deep Learning' faculty. To make use of the strength of the neural network model, which allows the recognition of patterns in data by modeling non-linear and complex relationships between variables, this study utilizes geographic coordinates (i.e. longitudinal/latitudinal points) as the locational factor of housing prices. Specifically, we built a dataset including structural and spatiotemporal factors based on the hedonic price model and compared the estimation performance of the models with and without geographic coordinate variables. The results show that high estimation performance can be achieved in ANN by explaining the spatial effect on housing prices through the geographic location.

• Journal of East-West Nursing Research
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• v.28 no.1
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• pp.31-40
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• 2022

Purpose: The purpose of this study was to identify factors affecting burnout of nurses caring for people with mental illness in general hospital wards. Methods: This study conducted a survey targeting 186 nurses in general wards with more than one year of clinical experience working at three general hospitals in Seoul, Korea. The collected data were analyzed using t-test, one-way ANOVA, Pearson's correlation coefficient, and multiple linear regression analysis with SPSS 21.0. Results: The factors influencing burnout were nursing work stress (β=.30, p<.001), attitude towards people with mental illness (β=-.25, p<.001), religion (β=-.21, p=.001), psychiatric nursing competency (β=-.16, p=.016), experience of nursing for people with mental illness (β=.14, p=.023), and gender (β=.14, p=.026), explaining 33.5% of the total variance (F=16.53, p<.001). Conclusion: The findings suggest that it is necessary to develop and apply an education program to lower nurses' work stress, to create positive attitude towards people with mental illness, and to enhance psychiatric nursing competency for prevention and mitigation of burnout of nurses caring for people with mental illness.

• Nutrition Research and Practice
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• v.16 no.2
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• pp.260-271
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• 2022

BACKGROUND/OBJECTIVES: The prevalence of obesity has been increasing in Korean children. As an unhealthy diet is known as one of the major determinants of childhood obesity, assessing and monitoring dietary fat intake of children is needed. SUBJECTS/METHODS: This analysis included 9,998 children aged 3-11 yrs from the 2007-2017 Korea National Health and Nutrition Examination Surveys. Dietary data were obtained from a single 24-h dietary recall. Intakes of total fat and fatty acids, including saturated fatty acid (SFA), monounsaturated fatty acid (MUFA), polyunsaturated fatty acid (PUFA), n-3 fatty acid (n-3 FA), and n-6 fatty acid (n-6 FA) were evaluated as the absolute amount (g) and proportion of energy from each fatty acid (% of energy). The total fat and SFA intake were also assessed according to compliance with dietary guidelines. Linear trends in the dietary fats intake across the survey period were tested using multiple regression models. RESULTS: Total fat intake significantly increased from 38.5g (20.3% of energy) to 43.4g (23.3% of energy) from 2007 to 2017. This increase was mainly accounted for the increases in intakes of SFA (7.2% to 8.4% of energy) and MUFA (6.2% to 7.5% of energy). PUFA intake increased from 4.4 to 4.7% of energy during the 11-yrs period: from 0.57 to 0.63% of energy for n-3 FA and from 3.8 to 4.1% of energy for n-6 FA. The proportions of children who consumed amounts exceeding the dietary guidelines for total fat and SFA significantly increased from 2007 to 2017, with increases from 9.8% to 17.4% for total fat and from 36.9% to 50.9% for SFA. CONCLUSIONS: Prominent increasing trends in the consumption of total fat and SFA but tiny change in n-3 FA intake were observed in Korean children. The healthy intake of dietary fats should be emphasized in this population.

• Smart Media Journal
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• v.11 no.4
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• pp.19-29
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• 2022

Recently, with the development of IoT technology, the number of farms using smart farms is increasing. Smart farms monitor the environment and optimise internal environment automatically to improve crop yield and quality. For optimized crop cultivation, researches on predict crop productivity are actively studied, by using collected agricultural digital data. However, most of the existing studies are based on statistical models based on existing statistical data, and thus there is a problem with low prediction accuracy. In this paper, we use various predition models for predicting the production and sales profits, and compare the performance results through models by using the agricultural digital data collected in the facility horticultural smart farm. The models that compared the performance are multiple linear regression, support vector machine, artificial neural network, recurrent neural network, LSTM, and ConvLSTM. As a result of performance comparison, ConvLSTM showed the best performance in R2 value and RMSE value.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.1-12
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• 2022

In the event of a maritime distress accident, rapid search and rescue operations using rescue assets are very important to ensure the safety and life of drowning person's at sea. In this paper, we analyzed the surface layer current in the northwest sea area of Ulleungdo by applying machine learning such as multiple linear regression, decision tree, support vector machine, vector autoregression, and LSTM to the meteorological information collected from the maritime observation buoy. And we predicted the drowning person's route at sea based on the predicted current direction and speed information by constructing each prediction model. Comparing the various machine learning models applied in this paper through the performance evaluation measures of MAE and RMSE, the LSTM model is the best. In addition, LSTM model showed superior performance compared to the other models in the view of the difference distance between the actual and predicted movement point of drowning person.

• Journal of Korean Clinical Nursing Research
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• v.28 no.1
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• pp.34-44
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• 2022

Purpose: The purpose of this study was to examine the level of Person-centered Critical Care Nursing (PCCN) and the factors influencing PCCN for nurses in Intensive Care Units (ICU). Methods: This study was designed by cross-sectional descriptive correlational study. The participants included 147 ICU nurses in two general hospitals in Seoul, Korea. Demographic characteristics, PCCN, communication skills, professionalism, and work environment were measured. The collected data were analyzed using descriptive statistical analysis, independent t-test, One-way ANOVA, Pearson's correlation coefficient, and stepwise multiple linear regression with the SPSS/Win 25.0 program. Results: The average age of the participants was 29.6±4.7 years and the mean work experience in the ICU was 4.67±3.52 years. The level of PCCN was 3.70±0.41, which was moderate to high, and it significantly showed a positive correlation with therapeutic communication skills (r=.66, p<.001), global interpersonal communication competence (r=.42, p<.001), professionalism (r=.38, p<.001), and work environment (r=.16, p=.048). The factors influencing PCCN were identified as therapeutic communication skill and global interpersonal communication competence (Adj R²=.45, p<.001). Conclusion: The findings of this study were confirmed that the strategies to promote PCCN are necessary to enhance therapeutic communication skill and global interpersonal communication competence. In addition, they may be particularly meaningful in providing basic data for nursing education and future intervention development research to promote PCCN for the ICU nurses. For improving PCCN for healthcare providers in ICU, further studies should be conducted to develop education and intervention programs.

• Journal of Korean society of Dental Hygiene
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• v.22 no.2
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• pp.143-151
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• 2022

Objectives: This study investigates dropout intention and the structural relationship between the dental hygienist role and satisfaction with the study major perceived by dental hygiene students. Methods: A survey was conducted on 269 dental hygiene students. The survey items covered general characteristics, department choice motivation, the desirability of dental hygienist career, practice clinical experience, perception of dental hygienist role, satisfaction with study major, and dropout intention. Independent sample t-test, one-way ANOVA, Mann-Whitney U test, multiple linear regression analysis, and structural equation modeling were used for statistical analysis. Results: The dropout intention level of dental hygiene students was 2.4 out of 5.0. Satisfaction with study major partially mediates perception of dental hygienist role and dropout intention (direct effect=0.182, p=0.024, indirect effect=-0.437, p=0.010). Perception of dental hygienist role (β=-0.255, p=0.010) and satisfaction with study major (β=-0.661, p=0.010) showed a negative relationship with dropout intention. The factor most affecting dropout intention was satisfaction with study major. Dropout intention was high when selecting a major based on external motivations (β=-0.448, p<0.001). Conclusions: Perception of dental hygienist role and satisfaction with study major directly or indirectly affect dropout intention. Therefore, improving satisfaction with study major and improving the perception of dental hygienists will help reduce dropout intention.