• Journal of Nutrition and Health
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• v.51 no.6
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• pp.526-537
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• 2018

Purpose: The purpose of the study was to identify women's osteoporosis knowledge, concerns about osteoporosis factors, and health behavior as well as to examine the relationship between these variables. Methods: The participants were 394 women in the Jeonbuk area. The data were analyzed using a t-test, ANOVA, Duncan test, and hierarchical regression analysis with SPSS v. 24.0. Results: The score for osteoporosis knowledge was 6.21 points out of a possible 12, the score for concern about osteoporosis factors was 26.50 points out of a possible 40. The score for the health behavior was 57.26 points out of a possible 85. The knowledge showed significant differences according age (p < 0.01), income (p < 0.05), education level (p < 0.01), drinking milk in childhood (p < 0.05), health interest (p < 0.05), and osteoporosis information (p < 0.01). The concern showed significant differences according to age (p < 0.001), income (p < 0.05), health interest (p < 0.001), osteoporosis information (p < 0.001), family history of osteoporosis (p < 0.05), and calcium medications (p < 0.001). The health behavior showed significant differences according to age (p < 0.001), income (p < 0.01), drinking milk in childhood (p < 0.05), health interest (p < 0.001), osteoporosis information (p < 0.01), and calcium medications (p < 0.01). Regression analysis showed that the concern about osteoporosis factors was the most influential variable on health behavior, followed by health interest of the subjects, age, and the osteoporosis knowledge. Conclusion: Therefore, it is necessary to consider educational programs on increasing interest in osteoporosis according to the age and health for improving the health behavior to prevent osteoporosis.

• Journal of Nutrition and Health
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• v.52 no.5
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• pp.501-513
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• 2019

Purpose: This study examined the menu pattern, food diversity, and satisfaction of parents with the snack menus of childcare centers provided by the Center for Children's Foodservice Management (CCFM) in Jeonbuk area. Methods: Data from 2,432 snack menus (1,321 for morning snacks and 1,111 for afternoon snacks) of March, June, September, and December 2017 from 13 CCFM in Jeonbuk area were analyzed. In addition, the participants for the survey were 247 parents in Jeonju and Kunsan. The data were analyzed using a t-test, ${\chi}^2$-test, and hierarchical regression analysis with SPSS v. 24.0. Results: Differences in the menu pattern and food diversity were observed between morning and afternoon snack menus. The majority of snack menus (61.6%) were one menu item. The percentage of 'G' (20.0%) was highest in the food group patterns. The morning snacks served mainly porridge, raw fruits, and milk, and the afternoon snacks served mainly flour-based foods, juices, and milk. The awareness level of parents about the snack menus of daycare centers was $4.09{\pm}0.82$, and its overall satisfaction was $4.06{\pm}0.69$. In the snack-quality attribute analysis, the hygiene of foods was the most important factor, and parents judged that they were doing well. Regression analysis showed that the hygiene of personnel was the most influential variable on the overall satisfaction, followed by balance with the main meal and the portion size. Conclusion: Therefore, it is important to establish snack menu guidelines considering the eating behaviors of the children and to strengthen hygiene for the increasing the satisfaction of various stakeholders in daycare centers.

• Journal of Nutrition and Health
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• v.53 no.6
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• pp.613-628
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• 2020

Purpose: The purpose of the study was to identify the health anxiety issues of middle-aged women, their health-promoting behavior, and quality of life as well as to examine the relationship between these variables. Methods: The participants were 334 women in Jeonbuk area. Demographic characteristics, the status of health anxiety, health-promoting behavior, and life quality was assessed using a self-administered questionnaire. The data were analyzed using a t-test, analysis of variance, Duncan test, and hierarchical regression analysis with SPSS ver. 24.0. Results: The score for health anxiety was 37.64 points out of a possible score of 60, and the score for health-promoting behavior was 79.18 points out of a possible score of 115. The score for the quality of life was 101.18 points out of a possible score of 150. The health anxiety scores showed significant differences, varying as per body mass index (BMI) (p < 0.05), income (p < 0.05), occupation (p < 0.05), disease (p < 0.05), satisfaction with weight (p < 0.05), and interest in weight control (p < 0.05). The health-promoting behavior showed significant differences according to age (p < 0.01), BMI (p < 0.01), income (p < 0.05), menses (p < 0.05), intake of dietary supplements (p < 0.05), perception of body image (p < 0.05), and satisfaction with weight (p < 0.05). The quality of life showed significant differences according to BMI (p < 0.05), income (p < 0.01), education level (p < 0.05), occupation (p < 0.05), disease (p < 0.05), and satisfaction with weight (p < 0.05). Regression analysis showed that health-promoting behavior was the most influential variable on the quality of life, followed by disease and health anxiety. Conclusion: Based on these results, we conclude that it is necessary to consider educational programs on improving the quality of life of middle-aged women according to the health anxiety levels and health-promoting behavior.

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.151-164
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• 2024

In water treatment plants supplying potable water, the management of chlorine concentration in water treatment processes involving pre-chlorination or intermediate chlorination requires process control. To address this, research has been conducted on water quality prediction techniques utilizing AI technology. This study developed an AI-based predictive model for automating the process control of chlorine disinfection, targeting the prediction of residual chlorine concentration downstream of sedimentation basins in water treatment processes. The AI-based model, which learns from past water quality observation data to predict future water quality, offers a simpler and more efficient approach compared to complex physicochemical and biological water quality models. The model was tested by predicting the residual chlorine concentration downstream of the sedimentation basins at Plant, using multiple regression models and AI-based models like Random Forest and LSTM, and the results were compared. For optimal prediction of residual chlorine concentration, the input-output structure of the AI model included the residual chlorine concentration upstream of the sedimentation basin, turbidity, pH, water temperature, electrical conductivity, inflow of raw water, alkalinity, NH₃, etc. as independent variables, and the desired residual chlorine concentration of the effluent from the sedimentation basin as the dependent variable. The independent variables were selected from observable data at the water treatment plant, which are influential on the residual chlorine concentration downstream of the sedimentation basin. The analysis showed that, for Plant, the model based on Random Forest had the lowest error compared to multiple regression models, neural network models, model trees, and other Random Forest models. The optimal predicted residual chlorine concentration downstream of the sedimentation basin presented in this study is expected to enable real-time control of chlorine dosing in previous treatment stages, thereby enhancing water treatment efficiency and reducing chemical costs.