• Journal of Nutrition and Health
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• v.51 no.1
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• pp.87-102
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• 2018

Purpose: The purpose of this study was to develop a valid instrument for measuring the dietary quality and behaviors of Korean elderly. Methods: The development of the Nutrition Quotient for Elderly (NQ-E) was conducted in three steps: item generation, item reduction, and validation. The 41 items of the NQ-E checklist were derived from a systematic literature review, expert in-depth interviews, statistical analyses of the fifth Korean National Health and Nutrition Examination Survey data, and national nutrition policies and recommendations. Pearson's correlation was used to determine the level of agreement between the questionnaires and nutrient intake level, and 24 items were selected for a nationwide survey. A total of 1,000 nationwide elderly subjects completed the checklist questionnaire. The construct validity of the NQ-E was assessed using confirmatory factor analysis, LISREL. Results: The nineteen checklist items were used as final items for NQ-E. Checklist items were composed of four-factors: food behavior (6 items), balance (4 items), diversity (6 items), and moderation (3 items). The standardized path coefficients were used as the weights of the items. The NQ-E and four-factor scores were calculated according to the obtained weights of the questionnaire items. Conclusion: NQ-E would be a useful tool for assessing the food behavior and dietary quality of the elderly.

• Journal of Ginseng Research
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• v.26 no.2
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• pp.67-73
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• 2002

This study was done in order to evaluate the of effects on the blood lipid profiles, the body weight and body fat in 28 healthy female volunteers who had over 30% body fat by the long term intake of red ginseng product. Subjects were divided into four groups (placebo group n=7, red ginseng product group; n=7, exorcise group; n=7, exercise & red ginseng product group; n=7). Blood sampling and measuring of the body fat were taken by pre-treatment, 3 weeks, and after 12 weeks. Statistical techniques for data analysis were applied one-way ANOVA and repeated measures ANOVA. The 5% level of significance was used as the critical level for this study. In summary of results, total cholesterol, triglyceride and low density lipoproprotein cholesterol were reduced in three groups (red ginseng product group, p<0.001, exercise group, p<0.01 ; exercise & red ginseng product group, p<0.001) except placebo group. HDL-C was improved in three groups (red ginseng product group, p<0.05; exercise group, p<0.01; exercise & red ginseng product group, p<0.001) except placebo group. Body weight, percent body fat and body fat mass were reduced in three groups (red ginseng product group, p<0.01, exercise group, p<0.01 ; exercise & red ginseng product group, p<0.001) except placebo group. Finally, lean body mass was improved in three groups (red ginseng product group, p<0.05; exercise group, p<0.01; exercise & red ginseng product group, p<0.001) except placebo group.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.731-745
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• 2021

In this study, the monthly temperature of the Han River basin was predicted by statistical multiple regression models that use global climate indices and weather data of the target region as predictors. The optimal predictors were selected through teleconnection analysis between the monthly temperature and the preceding patterns of each climate index, and forecast models capable of predicting up to 12 months in advance were constructed by combining the selected predictors and cross-validating the past period. Fore each target month, 1000 optimized models were derived and forecast ranges were presented. As a result of analyzing the predictability of monthly temperature from January 1992 to December 2020, PBIAS was -1.4 to -0.7%, RSR was 0.15 to 0.16, NSE was 0.98, and r was 0.99, indicating a high goodness-of-fit. The probability of each monthly observation being included in the forecast range was about 64.4% on average, and by month, the predictability was relatively high in September, December, February, and January, and low in April, August, and March. The predicted range and median were in good agreement with the observations, except for some periods when temperature was dramatically lower or higher than in normal years. The quantitative temperature forecast information derived from this study will be useful not only for forecasting changes in temperature in the future period (1 to 12 months in advance), but also in predicting changes in the hydro-ecological environment, including evapotranspiration highly correlated with temperature.

• Journal of Nutrition and Health
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• v.53 no.6
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• pp.596-612
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• 2020

Purpose: This study was conducted to evaluate the dietary behaviors of preschool children using the nutrition quotient for preschoolers (NQ-P) and investigate factors that influence NQ-P in preschool children. Methods: Subjects were 214 parents with children aged 3-5 years residing in Busan and Gyeongnam, Korea. The survey was conducted from March to April, 2019 using a questionnaire that included demographic characteristics, the NQ-P questions, and health consciousness. All data was statistically analyzed by the SPSS program (Ver 25.0) and the statistical differences in variables were evaluated by the chi-square test, Fisher's exact test, t-test, one-way ANOVA, and Tukey's multiple comparison test. Results: The mean score of NQ-P of the total subjects was 58.28, which was within the medium-low grade. The mean score of 'balance' was 60.08, 'moderation' was 47.64, and 'environment' was 67.83. The analysis of related-factors influencing NQ-P scores showed that there was a significant difference according to the frequency of dining out. The scores of the NQ-P (p < 0.05), moderation (p < 0.001), and environment (p < 0.05) were significantly higher in the 1-2 times per week group compare to 3-4 times and 5-6 times per week group. The scores of NQ-P (p < 0.01), environment (p < 0.01) were significantly higher in the high group of parents' health consciousness compared to the those with low health consciousness. Conclusion: According to the results of the evaluation by NQ-P, the dietary behaviors of preschool children residing in Busan and Gyeongnam need to be improved and monitored. For improving their eating behavior and nutritional health status, preschool children and their parents need proper nutrition education programs.

• The Journal of Korean Academy of Prosthodontics
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• v.59 no.1
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• pp.18-26
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• 2021

Purpose: Generally, patients are noticed to store denture in water when removed from the mouth. However, few studies have reported the advantage of volumetric change in underwater storage over dry storage. To be a reference in defining the proper denture storage method, this study aims to evaluate the volumetric change and dimensional deformation in case of underwater and dry storage. Materials and methods: Definitive casts were scanned by a model scanner, and denture bases were designed with computer-aided design (CAD) software. Twelve denture bases (upper 6, lower 6) were printed with 3D printer. Printed denture bases were invested and flasked with heat-curing method. 6 upper and 6 lower dentures were divided into group A and B, and each group contains 3 upper and 3 lower dentures. Group A was stored dry at room temperature, group B was stored underwater. Group B was scanned at every 24 hours for 28 days and scanned data was saved as stereolithography (SLA) file. These SLA files were analyzed to measure the difference in volumetric change of a month and Kruskal-Wallis test were used for statistical analysis. Best-fit algorithm was used to overlap and 3-dimensional color-coded map was used to observe the changing pattern of impression surface. Results: No significant difference was found in volumetric changes regardless of the storage methods. In dry-stored denture base, significant changes were found in the palate of upper jaw and posterior lingual border of lower jaw in direction away from the underlying tissue, maxillary tuberosity of upper jaw and retromolar pad area of lower jaw in direction towards the underlying tissue. Conclusion: Storing the denture underwater shows less volumetric change of impression surface than storing in the dry air.

• Journal of Nutrition and Health
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• v.54 no.4
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• pp.398-411
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• 2021

Purpose: The objective of this study was to investigate food and nutrition information utilization practices of adults aged between 20 and 30 years to provide the basic data for developing customized content. Methods: Statistical analyses were performed using the SPSS program (ver. 24.0) for the 𝛘²-test, t-test, one-way analysis of variance, and Duncan's multiple range test. Results: Of the 570 subjects surveyed, 45.4% were men, 54.6% were women, 66.3% were in their 20s, 33.7% were in their 30s, 41.4% were single-person households, and 58.6% lived with their families. On average, 14.2% of televisions (TVs), 26.0% of personal computers (PCs), and 63.7% of smartphones were used for more than three hours per day. 30.9% of respondents searched for food and nutrition information more than once a week. 70.0% of the respondents had then applied the information in real life and 54.7% of the respondents said they would share information with others. Information retrieval rate was in the order of 'restaurant (64.8%)', 'diet (57.5%)', and 'food recipes (55.7%)'. Overall satisfaction with food and nutrition information averaged 3.33 on a five-point scale. Satisfaction score was in the order of 'enough description and easy to understand (3.43)', 'matching title and content (3.35)', and 'providing new and novel information (3.22)'. Satisfaction scores were significantly higher in the group that searched for information (p < 0.001), the group that used the retrieved information in real life (p < 0.001), and the group that conveyed this information to others (p < 0.001). Conclusion: To improve information user satisfaction, it is necessary to provide customized information that fits the characteristics of information users. For this purpose, it is necessary to continuously conduct surveys and satisfaction evaluations for each target group.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of the korean academy of Pediatric Dentistry
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• v.50 no.3
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• pp.360-372
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• 2023

The aim of this study was to identify the current state of pediatric dentists, evaluate the adequacy of pediatric dentist supply and demand, and find out the perception of all pediatric dentists on the current state of pediatric dentists and policy establishment. An Online survey was conducted among pediatric dentists. The questionnaire was subdivided into 'general characteristics', 'number of dental treatments and working days per year', 'proportion of covered services', 'perceptions of supply and demand of pediatric dentists'. Through the Korean Academy of Pediatric Dentistry, the Health Insurance Review and Assessment Services, the National Health Insurance Service (NHIS), and the Korean Statistical Information Service, the current state of pediatric dentists, the number of claims for covered services, and the decrease in births per year were investigated. Dental clinics claiming to be pediatric dentistry reached half of all medical institutions, but only 3.78% of pediatric dentists actually worked. 61.36% of all pediatric dentists were concentrated in the metropolitan area, showing a national imbalance. Although the population of children and adolescents have continuously decreased over the past 20 years, the number of NHIS-covered services has shown a continuous increase. Over the past 10 years, the optimal supply of pediatric dentists has been maintained at around 4,000. According to the analysis, 92.15% of pediatric dentists thought that it was necessary to prepare policies and support measures at the government level. This study is expected to be used as basic data for establishing a demand estimation method for pediatric dentistry specialists in the future.

• Journal of the Korean Society of Radiology
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• v.17 no.2
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• pp.231-242
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• 2023

This study aims to provide basic data to reduce the incidence of radiation dermatitis and improve patient satisfaction by investigating the management status and satisfaction level of radiation dermatitis in patients undergoing radiation therapy. From October 28, 2022, to April 4, 2023, a survey was conducted on 137 breast cancer patients who received radiation therapy at G Hospital in Busan. The Radiation Therapy Oncology Group (RTOG) cutaneous acute radiotherapy toxicity score was used as the standard for measuring skin reactions, and the association between cancer stage and RTOG was analyzed. SPSS program (ver. 18.0) was used for statistical analysis. The frequency of radiation dermatitis occurrence was relatively low, with 73% in the RTOG 0-1 group and 27% in the 2-3 group. Patient satisfaction after radiation therapy varied significantly depending on the RTOG group, with lower levels of dermatitis resulting in higher satisfaction and higher levels resulting in dissatisfaction (p=0.001). Although there was no statistically significant difference in RTOG group and skin satisfaction depending on the frequency of aloe mist use (p=0.065), the group that used it 1-2 times a day (69.3%) showed a higher satisfaction level. The perceived effects of aloe mist use were statistically significant for decreasing heat sensation (p=0.001), pain (p=0.033), itching (p=0.001), and psychological stability (p=0.027), especially in the higher RTOG groups. Additionally, as cancer stage increased, the severity of radiation dermatitis also increased, which was statistically significant (p=0.001). In conclusion, radiation dermatitis is the most common side effect of radiation therapy, and it can appear in various forms depending on individual skin sensitivity and external factors during treatment. Adequate education before treatment and the use of MD Cream and aloe vera mist are recommended to reduce the incidence and manage radiation dermatitis effectively.