• Korean Journal of Community Nutrition
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• v.29 no.1
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• pp.51-64
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• 2024

Objectives: This study examined the association between ultra-processed food (UPF) consumption and chronic diseases in elderly Koreans. Methods: Data from the 2019-2021 Korea National Health and Nutrition Examination Survey were analyzed. Dietary intake and UPF consumption were assessed using the NOVA food classification based on 24-hour recall data from 3,790 participants (aged 65+ years). Participants were divided into 4 groups based on the quartile of energy intake from UPFs. Regions were classified as urban or rural. Multivariable logistic regression was employed to estimate the adjusted odds ratios (AORs) with 95% confidence intervals (CIs) after controlling for potential confounders. Results: Among the participants, 71.3% resided in urban and 28.7% in rural areas. Compared to the urban elderly, rural participants tended to be older, have lower education and income levels, be more likely to live in single-person households, and have a higher smoking rate (P < 0.05). Urban elderly consumed more UPFs daily (146.1 g) compared to rural residents (126.6 g; P < 0.05). "Sugar-sweetened beverages" were the most consumed category in both regions. "Sweetened milk and its products" and "traditional sauces" were prominent in urban areas, while rural elderly consumed more "traditional sauces" and "distilled alcoholic beverages." Rural areas also had a higher carbohydrate-to-calorie ratio than urban areas. Compared to the lowest quartile of UPF intake, the highest quartile was significantly associated with impaired fasting glucose only in rural areas (AOR, 1.48; 95% CI, 1.00-2.19; P for trend = 0.0014). No significant associations were observed for diabetes in either urban or rural areas. Conclusions: This study suggests that high intake of UPFs is associated with increased odds of impaired fasting glucose in rural elderly. Further research is needed to elucidate the specific negative health effects of UPFs in different populations, and targeted efforts should promote healthy diets in both urban and rural areas.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.26 no.3
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• pp.211-219
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• 2022

Backgrounds/Aims: Historically, incidence and survival analysis and annual traits for primary liver cancer (LC) has not been investigated in a population-based study in Korea. The purpose of the current study is to determine incidence, survival rate of patients with primary LC in Korea. Methods: We conducted a retrospective cohort study using Korea Central Cancer Registry based on the Korea National Cancer Incidence Database. Statistical analysis including crude rate and age-standadized rate (ASR) of incidence and mortality was performed for LC patients registered with C22 code in International Classification of Diseases, tenth revision from 1999 to 2019. Subgroup analysis was performed for hepatocellular carcinoma (HCC, C22.0) and intrahepatic cholangiocarcinoma (IHCC, C22.1). Results: The crude incidence rate of HCC (21.0 to 22.8 per 100,000) and IHCC (2.3 to 5.6 per 100,000) increased in the observed period from 1999 to 2019. The ASR decreased in HCC (20.7 to 11.9 per 100,000) but remained unchanged in IHCC (2.4 to 2.7 per 100,000). The proportion of HCC patients diagnosed in early stages (localized or regional Surveillance, Epidemiology, and End Results or SEER stage) increased significantly over time. As expected, 5-yeat survival rate of HCC was greatly improved, reaching 42.4% in the period between 2013 and 2019. This trait was more prominent in localized SEER stage. On the other hand, the proportion of IHCC patients diagnosed in localized stage remained unchanged (22.9% between 2013 and 2019), although ASR and 5-year survival rate showed minor improvements. Conclusions: A great improvement in survival rate was observed in patients with newly diagnosed HCCs. It was estimated to be due to an increase in early detection rate. On the contrary, detection rate of an early IHCC was stagnant with a minor improvement in prognosis.

• Korean Journal of Community Nutrition
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• v.29 no.4
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• pp.348-357
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• 2024

Objectives: The total population of Korea began to decline in 2019; in particular, the population in rural areas has been rapidly decreasing and is aging. Therefore, the government has designated depopulation areas and is seeking ways to support them. To assess whether health disparities exist between areas with population decline and those without, this study used community health survey data to observe temporal changes in health behaviors between the two types of areas. Methods: The analysis used Community Health Survey data from 2010 to 2019, and regional classification was divided by depopulation areas designated by the Ministry of the Interior and Safety. Trends in health behavior and chronic disease prevalence between depopulation and non-depopulation areas were analyzed. All analyses were conducted using complex sample analysis procedures in SAS 9.4 software. Results: The smoking rate steadily decreased in both depopulation and non-depopulation areas, whereas the high-risk drinking rate increased slightly. The walking practice rate did not improve in depopulation areas compared to non-depopulation areas. Furthermore, nutritional labeling usage rate was consistently lower in depopulation areas than in non-depopulation areas, with the gap being the largest. The prevalence of obesity, diabetes, and hypertension showed that the gap between depopulation and non-depopulation areas is continuously increasing. Conclusions: Health behaviors in depopulation areas have not improved, and the prevalence of chronic diseases is increasing rapidly. Therefore, the demand for health care services that support healthy lifestyle practices and chronic disease management in these areas is expected to increase.

• Journal of Intelligence and Information Systems
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• v.26 no.3
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• pp.149-169
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• 2020

"The Urban Renewal New Deal project", one of the government's major national projects, is about developing underdeveloped areas by investing 50 trillion won in 100 locations on the first year and 500 over the next four years. This project is drawing keen attention from the media and local governments. However, the project model which fails to reflect the original characteristics of the area as it divides project area into five categories: "Our Neighborhood Restoration, Housing Maintenance Support Type, General Neighborhood Type, Central Urban Type, and Economic Base Type," According to keywords for successful urban regeneration in Korea, "resident participation," "regional specialization," "ministerial cooperation" and "public-private cooperation", when local governments propose urban regeneration projects to the government, they can see that it is most important to accurately understand the characteristics of the city and push ahead with the projects in a way that suits the characteristics of the city with the help of local residents and private companies. In addition, considering the gentrification problem, which is one of the side effects of urban regeneration projects, it is important to select and implement urban regeneration types suitable for the characteristics of the area. In order to supplement the limitations of the 'Urban Regeneration New Deal Project' methodology, this study aims to propose a system that recommends urban regeneration types suitable for urban regeneration sites by utilizing various machine learning algorithms, referring to the urban regeneration types of the '2025 Seoul Metropolitan Government Urban Regeneration Strategy Plan' promoted based on regional characteristics. There are four types of urban regeneration in Seoul: "Low-use Low-Level Development, Abandonment, Deteriorated Housing, and Specialization of Historical and Cultural Resources" (Shon and Park, 2017). In order to identify regional characteristics, approximately 100,000 text data were collected for 22 regions where the project was carried out for a total of four types of urban regeneration. Using the collected data, we drew key keywords for each region according to the type of urban regeneration and conducted topic modeling to explore whether there were differences between types. As a result, it was confirmed that a number of topics related to real estate and economy appeared in old residential areas, and in the case of declining and underdeveloped areas, topics reflecting the characteristics of areas where industrial activities were active in the past appeared. In the case of the historical and cultural resource area, since it is an area that contains traces of the past, many keywords related to the government appeared. Therefore, it was possible to confirm political topics and cultural topics resulting from various events. Finally, in the case of low-use and under-developed areas, many topics on real estate and accessibility are emerging, so accessibility is good. It mainly had the characteristics of a region where development is planned or is likely to be developed. Furthermore, a model was implemented that proposes urban regeneration types tailored to regional characteristics for regions other than Seoul. Machine learning technology was used to implement the model, and training data and test data were randomly extracted at an 8:2 ratio and used. In order to compare the performance between various models, the input variables are set in two ways: Count Vector and TF-IDF Vector, and as Classifier, there are 5 types of SVM (Support Vector Machine), Decision Tree, Random Forest, Logistic Regression, and Gradient Boosting. By applying it, performance comparison for a total of 10 models was conducted. The model with the highest performance was the Gradient Boosting method using TF-IDF Vector input data, and the accuracy was 97%. Therefore, the recommendation system proposed in this study is expected to recommend urban regeneration types based on the regional characteristics of new business sites in the process of carrying out urban regeneration projects."

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.

• Economic and Environmental Geology
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• v.45 no.1
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• pp.31-40
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• 2012

This paper examined the characteristics of ductile and brittle structural elements with detailed mapping by lithofacies classification to clarify the relationship between the geological structure and the geochemical high-grade uranium anormal zone and to provide the basic information on the flow of groundwater in the eastern area of Shinbo mine, Jinan-gun, Jeollabuk-do, Korea. It indicates that this area is mainly composed of Precambrian quartzite, metapelite, metapsammite, which show a zonal distribution of mainly ENE-WSW trend, and age unknown pegmatite and Cretaceous porphyry which intrude them. But the Cretaceous Jinan Group which unconformably covers them, contrary to assumption, could not be observed. The main ductile deformation structures of Precambrian metasedimentary rocks were formed at least through three phases of deformation [ENE striking regional foliation (D1) -> ENE or EW striking crenulation foliation (D2) -> WNW or EW trending open, tight, kink folds (D3)]. The predominant orientation of S1 regional foliation strikes ENE and dips south, being similar to the zonal distribution of Precambrian metasedimentary rocks. Most predominant orientation of high-angled brittle fracture (dip angle ${\geq}45^{\circ}$) [ENE (frequency: 24.3%) > NS (23.9%) > (N)NW (18.8%) > WNW (16.9%) > NE (16.1%) fracture sets in descending frequency order], which is closely related to the flow of groundwater, strikes ENE and dips south. It also agrees with the zonal distribution of metasedimentary rocks and the predominant orientation of S1 regional foliation. The next one strikes NS and dips east or west. Considering the controlling factor of the geochemical uranium anormal zone in the Shinbo mine and its eastern areas from the above structural data. the uranium source rock in these areas might be pegmatite and the geochemical uranium anormal zone in the Sinbo mine area could be formed by an secondary enrichment through the flow of pegmatite aquifer's groundwater into the Sinbo mine area like the previous research's result.

• Korean Journal of Culture and Social Issue
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• v.15 no.2
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• pp.291-318
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• 2009

The purpose of this study was to find out the impact of psycho-social factors (social relationship and personal) as well as illness history and economic status on physical health and subjective well-being among the retired Koreans elderly. Data were collected from 1,315 elders (mean age = 72.70yrs) residing in Seoul and Chuncheon regional area via interviews(Time 1), and them were re-interviewed two year later(Time 2). Multiple regression analyses indicated that the retired elders' illness history, economic status, marital satisfaction, fulfillment of self-esteem need, drinking behavior, positive affectivity, negative affectivity and physical health to predict their subjective well-being at Time 1(R²=.705). The retired elders' economic status, marital satisfaction, positive affectivity, negative affectivity and physical health to predict their subjective well-being at Time 2(R²=.418). The retired elders' illness history, economic status, expectations for one's offspring, drinking behavior and subjective well-being to predict their physical health at Time 1(R²=.364). And the retired elders' illness history, economic status, marital satisfaction, positive affectivity and negative affectivity to predict their physical health at Time 2(R²=.265). In case of retired elderly, suggested for the psycho-social determenants of subjective well-being and physical health by occupational classification. The implications of this study and the suggestions for furture study were discussed.

• Geophysics and Geophysical Exploration
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• v.13 no.1
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• pp.102-108
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• 2010

The internal structure of a river embankment must be delineated as part of investigations to evaluate its safety. Geophysical methods can be most effective means for that purpose, if they are used together with geotechnical methods such as the cone penetration test (CPT) and drilling. Since the dyke body and subsoil in general consist of material with a wide range of grain size, the properties and stratification of the soil must be accurately estimated to predict the mechanical stability and water infiltration in the river embankment. The strength and water content of the levee soil are also parameters required for such prediction. These parameters are usually estimated from CPT data, drilled core samples and laboratory tests. In this study we attempt to utilise geophysical data to estimate these parameters more effectively for very long river embankments. S-wave velocity and resistivity of the levee soils obtained with geophysical surveys are used to classify the soils. The classification is based on a physical soil model, called the unconsolidated sand model. Using this model, a soil profile along the river embankment is constructed from S-wave velocity and resistivity profiles. The soil profile thus obtained has been verified by geotechnical logs, which proves its usefulness for investigation of a river embankment.

• Journal of Environmental Impact Assessment
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• v.24 no.3
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• pp.287-299
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• 2015

If the Nakdong River estuary is to be sustainable, land use management practices need to consider bird habitat requirements, especially given that the area serves as an important migratory bird sanctuary. In this study, bird habitats found in the Nakdong River estuary were classified into 11 different types including Phragmites australiss, mud flat, farmland, open surface in freshwater, sand bar, riparian forest, Scirpus planiculmis, waterway, construction, grasslands, and open surface in sea or brackish water. Taking into consideration the regional characteristics, habitat properties, and landscape indices, a total of 12 study sites were analyzed. Mud flat, construction, farmland, and P. australis account for 80% of the total land area. The high area ratio of construction and farmland to other types of habitat revealed a high amount of historical human activity and intervention in the area. Both patch numbers as well as patch density were high in West Nakdong River, Samrak Waterfront, Maekdo River, and Daejeo Floodgate, with these areas showing the greatest fragmentation as well. Total numbers of species and individuals had a positive correlation with the area and the number of habitat types. Findings suggest that protecting the habitat area, especially in S. planiculmis, is the most important factor for bird habitat management and that future development could result in habitat loss, having a profoundly adverse impact on bird populations. Therefore, it is important that the total area should be carefully protected by land use regulations in order to ensure that the Nakdong River estuary maintains its functional integrity as a migratory bird sanctuary.

• Asian Journal of Atmospheric Environment
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• v.5 no.4
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• pp.278-291
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• 2011

Korea has experienced dramatic development and has become highly industrialized and urbanized during the past 40 years, which has resulted in rapid economic growth. Due to the industrialization and urbanization, however, air pollutant emission sources have increased substantially. Rapid increases in emission sources have caused Korea to suffer from serious air pollution. An air pollutant emissions inventory is one set of essential data to help policymakers understand the current status of air pollution levels, to establish air pollution control policies and to analyze the impacts of implementation of policies, as well as for air quality studies. To accurately and realistically estimate administrative district level air pollutant emissions of Korea, we developed a Korean Emissions Inventory System named the Clean Air Policy Support System (CAPSS). In CAPSS, emissions sources are classified into four levels. Emission factors for each classification category are collected from various domestic and international research reports, and the CAPSS utilizes various national, regional and local level statistical data, compiled by approximately 150 Korean organizations. In this paper, we introduced for the first time, a Korean national emissions inventory system and release Korea's official 2007 air pollutant emissions for five regulated air pollutants.