• Journal of Nutrition and Health
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• v.56 no.3
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• pp.264-276
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• 2023

Purpose: Food environmental factors related to food insecurity affect household food intake in several socio-ecological aspects. This study explores the relationship between food environment factors and food insecurity in households with married immigrant women. Methods: From November 2018 to February 2020, a survey was conducted enrolling 249 married immigrant women residing in the metropolitan areas of South Korea. In the final analysis, 229 subjects were divided into 2 groups classified as food security (n = 154) and food insecurity (n = 75), as assessed by the score of food security. Three aspects of food environments were measured: built·natural, political·economic, and socio-cultural Results: Food environments were significantly different between food security and food insecurity groups, as follows: the number of foods market and their distance from the home and food status for the last week at home in the built·natural domain; monthly cost of food purchase and experience for food assistance in the political·economic domain; total score of social support, parenting, and cooking skills in the socio-cultural domain. A stepwise multivariate linear regression model showed a negative association between the food insecurity score with social support from family and food inventory status in the last week. After adjusting for confounders, a positive association was obtained between the experience of a food support program. The final regression model explains about 30% of the relationship obtained in the three food environment domains and food insecurity (p < 0.001). Conclusion: Not only economic factors, which are common determinants of household food insecurity, but socio-cultural factors such as social support also affect household food insecurity. Therefore, plans for implementing a food assistance program to improve food insecurity for households with immigrant women should consider financial support as well as other comprehensive aspects, including socio-cultural domain such as social support from family and community.

• Journal of the Korean Society of International Agriculture
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• v.31 no.3
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• pp.278-285
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• 2019

The experiment was implemented to gain basic data for stable proso millet (Panicum miliaceum L.) production through germination viability and establishment uniformity based on seed refining under seed selection with specific gravity of proso millet seed. Seeds of three proso millet cultivars were carefully selected by adjusting NaCl into 9 levels (1.000, 1.005, 1.010, 1.015, 1.020, 1.025, 1.030, 1.035, 1.04 g·L^-1), and their standard germination rate, seeding quality and field emergence rate were investigated. Proso millets showed heavier thousand seed weight as specific gravity high. In standard germination, all of the 3 cultivars of selected seeding showed at least 91% germination rate. With respect to the 1.040 g·L^-1 selection by specific gravity, 'Hwanggeum' had 98.9%, 'Ibaekchal', 99.4%, and 'Hwangsilchal', 98.9%, recording the highest. Seeding quality was investigated and it was found that, compared with the non-selected seeds, selected seeds had longer plant heights and averaged 2.0, 2.2 and 1.9 cm, respectably. In terms of leaf length, only 'Hwanggeum' and 'Ibaekchal' showed distinctive differences. No significance was found in stem diameter and SPAD. The seeds selected using specific gravity all had longer or heavier average root length, root weight and stem weight. Compared with non-selected seeds, the specific gravity of 1.040 g·L^-1 had longer root lengths of 2.9, 3.0 and 2.9 cm. 1.040 g·L^-1 was also found to have the heaviest root weight, and stem weight. The field emergence viability investigation found significance solely in the mean emergence time (MET) by the cultivar. High significance was found in percent emergence (PE) and emergence rate index (ERI) in the specific gravity for the selection of seeds. All of the three cultivars showed higher PE and ERI as specific gravity selection high. The 1.040 g·L^-1 specific gravity selection had the highest PE of 90.7, 85.3 and 92.8%. For productivity increase based on improved proso millet germination rate and enhanced establishment uniformity, it seems favorable to employ 1.040 g·L^-1 specific gravity selection using salt solution.

• Journal of IKEEE
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• v.27 no.4
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• pp.464-471
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• 2023

In this paper, we propose a method to reduce age distortion in facial expression image generation using StyleGAN Encoder. The facial expression image generation process first creates a face image using StyleGAN Encoder, and changes the expression by applying the learned boundary to the latent vector using SVM. However, when learning the boundary of a smiling expression, age distortion occurs due to changes in facial expression. The smile boundary created in SVM learning for smiling expressions includes wrinkles caused by changes in facial expressions as learning elements, and it is determined that age characteristics were also learned. To solve this problem, the proposed method calculates the correlation coefficient between the smile boundary and the age boundary and uses this to introduce a method of adjusting the age boundary at the smile boundary in proportion to the correlation coefficient. To confirm the effectiveness of the proposed method, the results of an experiment using the FFHQ dataset, a publicly available standard face dataset, and measuring the FID score are as follows. In the smile image, compared to the existing method, the FID score of the smile image generated by the ground truth and the proposed method was improved by about 0.46. In addition, compared to the existing method in the smile image, the FID score of the image generated by StyleGAN Encoder and the smile image generated by the proposed method improved by about 1.031. In non-smile images, compared to the existing method, the FID score of the non-smile image generated by the ground truth and the method proposed in this paper was improved by about 2.25. In addition, compared to the existing method in non-smile images, it was confirmed that the FID score of the image generated by StyleGAN Encoder and the non-smile image generated by the proposed method improved by about 1.908. Meanwhile, as a result of estimating the age of each generated facial expression image and measuring the estimated age and MSE of the image generated with StyleGAN Encoder, compared to the existing method, the proposed method has an average age of about 1.5 in smile images and about 1.63 in non-smile images. Performance was improved, proving the effectiveness of the proposed method.

• Korean Journal of Psychosomatic Medicine
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• v.31 no.2
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• pp.63-71
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• 2023

Urinary incontinence (UI), affecting 3%-11% of males and 25%-45% of females globally, is expected to rise with an aging population. It significantly impacts mental health, causing depression, stress, and reduced quality of life. UI can exacerbate psychiatric conditions, affecting treatment compliance and effectiveness. It is categorized into transient and chronic types. Transient UI, often reversible, is caused by factors summarized in the acronym DIAPPERS: Delirium, Infection, Atrophic urethritis/vaginitis, Psychological disorders, Pharmaceuticals, Excess urine output, Restricted mobility, Stool impaction. Chronic UI includes stress, urge, mixed, overflow, functional, and persistent incontinence. Drug-induced UI, a transient form, is frequently seen in psychiatric treatment. Antipsychotics, antidepressants, and other psychiatric medications can cause UI through various mechanisms like affecting bladder muscle tone, altering nerve reflexes, and inducing other conditions like diabetes or epilepsy. Specific drugs like lithium and valproic acid have also been linked to UI, though mechanisms are not always clear. Managing UI in psychiatric patients requires careful monitoring of urinary symptoms and judicious medication management. If a drug is identified as the cause, options include discontinuing, reducing, or adjusting the dosage. In cases where medication continuation is necessary, additional treatments like desmopressin, oxybutynin, trihexyphenidyl, or amitriptyline may be considered.

• Journal of Nutrition and Health
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• v.56 no.6
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• pp.702-713
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• 2023

Purpose: Depression is a prevalent mental health concern globally including South Korea. Given the growing interest in the relationship between diet and mental health, this study aimed to investigate the association between seafood consumption and depression among Korean adults. Methods: A cross-sectional analysis was conducted using data from the Korea National Health and Nutrition Examination Survey (KNHANES, 2014-2020). The study included 18,149 participants (7,541 men and 10,608 women) aged 19 years and older who completed the Patient Health Questionnaire (PHQ-9). Seafood intake levels were assessed using a oneday 24-hour dietary recall, and participants were categorized into three tertiles by gender. Depression status was determined using the PHQ-9 scores and the self-report of the doctor's diagnosis and treatment. Multivariable logistic regression analysis was performed to assess the association between seafood consumption and depression in both genders. Results: Participants with a higher seafood intake had a significantly lower nutritional density of total fat, while the nutritional density of omega-3 polyunsaturated fatty acids was significantly higher. The prevalence of depression was significantly lower in the highest tertile of seafood consumption compared to the lowest tertile in both men (p < 0.001) and women (p < 0.001). After adjusting for confounding factors, the highest tertile of seafood consumption demonstrated a decreased risk of depression compared to the lowest tertile in men (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.51-0.99; p-trend = 0.020) and women (OR, 0.73; 95% CI, 0.59-0.91; p-trend = 0.004). Conclusion: The findings of this study suggest that consuming seafood rich in omega-3 fatty acids may potentially reduce the risk of depression in the adult population.

• Korean Journal of Remote Sensing
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• v.39 no.5_4
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• pp.1097-1109
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• 2023

Urban is an area where small-scale changes to individual buildings occur frequently. An existing urban building database requires periodic updating to increase its usability. However, there are limitations in data collection for building changes over a wide urban. In this study, we check the possibility of detecting building changes and updating a building database by using satellite images that can capture a wide urban region by a single image. For this purpose, building areas in a satellite image are first extracted by projecting 3D coordinates of building corners available in a building database onto the image. Building areas are then divided into roof and facade areas. By comparing textures of the roof areas projected, building changes such as height change or building removal can be detected. New height values are estimated by adjusting building heights until projected roofs align to actual roofs observed in the image. If the projected image appeared in the image while no building is observed, it corresponds to a demolished building. By checking buildings in the original image whose roofs and facades areas are not projected, new buildings are identified. Based on these results, the building database is updated by the three categories of height update, building deletion, or new building creation. This method was tested with a KOMPSAT-3A image over Incheon Metropolitan City and Incheon building database available in public. Building change detection and building database update was carried out. Updated building corners were then projected to another KOMPSAT-3 image. It was confirmed that building areas projected by updated building information agreed with actual buildings in the image very well. Through this study, the possibility of semi-automatic building change detection and building database update based on single satellite image was confirmed. In the future, follow-up research is needed on technology to enhance computational automation of the proposed method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

• Journal of Nutrition and Health
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• v.56 no.5
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• pp.554-572
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• 2023

Purpose: The purpose of the study was to analyze the priorities for educational content regarding precautions to be taken to prevent safety accidents for employees in school foodservice using the Borich priority formula and the Locus for Focus model. Methods: A survey was conducted in February 2019 on 194 employees in elementary school and 122 employees in middle- and high school foodservice in the Jeonbuk area. Demographic characteristics, status of safety accidents, safety education, and their importance and performance levels were assessed using a self-administered questionnaire. The priorities for the educational content on precautions to prevent safety accidents were based on a 3-step analysis method, including the paired sample t-test, Borich priority formula, and the Locus for Focus Model. Results: The average perceived importance of the precautions to be taken against safety accidents of employees in elementary-, middle-, and high schools was higher compared to the average performance of the employees (p < 0.001). The top priority for elementary school employees was caution against falls during the cleaning of the gas hood and the trench in the kitchen. In addition, 'awareness of chemical signs' was added as one of the top priorities of middle- and high school employees. The second highest priority items were 'do stretching', 'safely adjusting workbench height', 'keeping the right attitude', 'using assistive devices when moving heavy things', and 'checking the material safety data sheet', which were the same for all elementary, middle- and high school employees. Conclusion: Thus, to improve the educational preparedness of employees in the area of safety precautions, eight safety/accident prevention items should be included in the safety education program.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• KDI Journal of Economic Policy
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• v.46 no.1
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• pp.91-129
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• 2024

The Bank of Korea, through a legal amendment in 2011 following the financial crisis, was entrusted with the additional responsibility of financial stability beyond its existing mandate of price stability. Since then, concerns have been raised about the prolonged increase in household debt compared to income conditions, which could constrain consumption and growth and increase the possibility of a crisis in the event of negative economic shocks. The current accumulation of financial imbalances suggests a critical period for the government and central bank to be more vigilant, ensuring it does not impede the stable flow of our financial and economic systems. This study examines the applicability of the Integrated Inflation Targeting (IIT) framework proposed by the Bank for International Settlements (BIS) for macro-financial stability in promoting long-term economic stability. Using VAR models, the study reveals a clear increase in risk appetite following interest rate cuts after the financial crisis, leading to a rise in household debt. Additionally, analyzing the central bank's conduct of monetary policy from 2000 to 2021 through DSGE models indicates that the Bank of Korea has operated with a form of IIT, considering both inflation and growth in its policy decisions, with some responsiveness to the increase in household debt. However, the estimation of a high interest rate smoothing coefficient suggests a cautious approach to interest rate adjustments. Furthermore, estimating the optimal interest rate rule to minimize the central bank's loss function reveals that a policy considering inflation, growth, and being mindful of household credit conditions is superior. It suggests that the policy of actively adjusting the benchmark interest rate in response to changes in economic conditions and being attentive to household credit situations when household debt is increasing rapidly compared to income conditions has been analyzed as a desirable policy approach. Based on these findings, we conclude that the integrated inflation targeting framework proposed by the BIS could be considered as an alternative policy system that supports the stable growth of the economy in the medium to long term.