• The Journal of Korean Physical Therapy
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• v.32 no.6
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• pp.329-334
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• 2020

Purpose: The purpose of this study is to provide basic evidence on the need to approach osteoarthritis patients through a psychological factors-considering rehabilitation program by understanding how activity restrictions in osteoarthritis affect health-related quality of life and depression. Methods: This study assessed 3,761 osteoarthritis patients from the Korea National Health and Nutrition Examination Survey. The subjects were divided into two categories: with and without activity restriction. Results: The prevalence of osteoarthritis in women was higher than that of men (men: 19.7%; women: 80.3%), and high BMI increased the prevalence of osteoarthritis. The EQ-5D index of subjects with activity restriction was 0.84±0.18 (points), while in those without activity restriction was 0.93±0.12, and the diagnosis of depression was 15.8%, 8.2%. There was a statistically significant difference in the odds ratio for each item in the EQ-5D. Moreover, the odds ratio for depression with activity restriction was 2.098 compared with no activity restriction. Conclusion: Activity restriction of osteoarthritis patients significantly decreases the health-related quality of life and increase the probability of depression. Therefore, early diagnosis of depression symptoms to prevent deterioration of symptoms in patients with osteoarthritis and to increase compliance with rehabilitation treatment, and to provide arbitration, including treatment that can alleviate depression.

• Earthquakes and Structures
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• v.22 no.4
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• pp.387-399
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• 2022

To study the seismic damage of masonry structures and understand the characteristics of the multi-intensity region, according to the Dujiang weir urbanization of China Wenchuan earthquake, the deterioration of 3991 masonry structures was summarized and statistically analysed. First, the seismic damage of multistory masonry structures in this area was investigated. The primary seismic damage of components was as follows: Damage of walls, openings, joints of longitudinal and transverse walls, windows (lower) walls, and tie columns. Many masonry structures with seismic designs were basically intact. Second, according to the main factors of construction, seismic intensity code levels survey, and influence on the seismic capacity, a vulnerability matrix calculation model was proposed to establish a vulnerability prediction matrix, and a comparative analysis was made based on the empirical seismic damage investigation matrix. The vulnerability prediction matrix was established using the proposed vulnerability matrix calculation model. The fitting relationship between the vulnerability prediction matrix and the actual seismic damage investigation matrix was compared and analysed. The relationship curves of the mean damage index for macrointensity and ground motion parameters were drawn through calculation and analysis, respectively. The numerical analysis was performed based on actual ground motion observation records, and fitting models of PGA, PGV, and MSDI were proposed.

• Computers and Concrete
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• v.29 no.6
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.1
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• pp.21-29
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• 2023

This paper reviews the global effect of the coronavirus disease 2019 (COVID-19) pandemic on the mental health of children and adolescents in South Korea, the U.S., Japan, and China. We reviewed research on deteriorated mental health, including increased suicide, suicidal thoughts, and self-harm. Various studies have shown that students' mental health issues, such as depression and anxiety, have worsened during the COVID-19 pandemic. Furthermore, the number of students who committed suicide has significantly increased in the U.S. and Japan. Factors such as prior mental health status, change in daily routine, reduced physical activity, excessive screen time, overuse of electronic devices, and reduced social support have been reported to have a significant effect. The chain of deteriorating mental health among the youth began at the onset of COVID-19, social distancing, and school closure. As youths began to stay at home instead of going to school, they lost opportunities to connect with their friends or teachers, who could provide support outside of their homes. Young people spent less time on physical activity and more time online, which damaged their sleeping schedule and daily routine. In preparing for the post-pandemic phase, we should thoroughly analyze the long-term effects of the pandemic on youth mental health, while simultaneously tackling current imminent issues.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.2
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• pp.69-75
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• 2023

Objectives: The coronavirus disease 2019 pandemic and its associated societal responses are anticipated to have wide-ranging effects on youth development and mental health. Depression, anxiety, and attention deficit hyperactivity disorder (ADHD) are the three most common mental health problems among university students. Many factors that can threaten mental health during the outbreak affect these three conditions, as well as sleep conditions, in undergraduate students. Thus, determining how these abrupt changes in students' circumstances impact their mental health is very important from a public health perspective. Methods: We investigated the usual conditions and changes in ADHD symptoms during the outbreak, in relation to depressive and sleep-related symptoms among undergraduate students. A total of 252 students, primarily juniors, completed the online survey. Results: The results showed that 12% of the students exceeded the cut-off score of the ADHD questionnaire before the pandemic. Approximately 6%-21% of the university students, especially those with ADHD traits, rated their ADHD behaviors as worse during the outbreak than that before the outbreak. Conclusion: Female students and undergraduates with ADHD traits are more susceptible to experiencing further deterioration of ADHD (inattention) symptoms during the pandemic. In cases where it is difficult to intervene with ADHD symptoms, approaching circadian rhythm or depression will be of considerable clinical use.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.922-931
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• 1993

Boundary element method was used to solve heat conduction problem for predicting temperature distribution in grain storage bin. Temperature of grain in storage is one of the three main abiotic factors, besides the intergranular gas composition and the grain moisture content, that determine the keeping quality and control measures used to protect grain from insects and damaging microflora. Collecting the temperature data at various points in the storage bins at different time of the day over a period of time is one way of finding the temperature distribution, this method requires a lot of time, cost and labour and less efficient. However data so collected serve useful purpose of being used to validate predicted temperature distribution using mathematical models. Mathematical models based on physical principles can potentially predict with accuracy the temperature distribution in a grain storage bin. Using the boundary element model the effect of bin wall material, ambient emperature, bin size etc. on temperature distribution can be studied. A knowledge of temperature distribution in stored grain not only helps in identifying active deterioration , but also gives an indication of potential for detection.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.188-189
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• 2020

In the field of architecture, concrete and steel bars are the most common and popular combinations. The relationship between the two in a structure is a complementary good that increases in utility when consuming both materials at the same time. However, the combination of the two, which has been perceived as semi-permanent, often faces repairs or reconstruction without its lifespan reaching decades. There are a number of deterioration factors at work for the reason for this phenomenon. Among them, the neutralization of concrete in particular refers to the process in which calcium hydroxide inside concrete reacts with carbon dioxide and loses alkalinity, which creates a corrosive environment for rebars inside concrete, causing serious damage to concrete. In this study, we intend to use a multi-physical analysis program using finite element analysis method to analyze the degree of carbonation according to the internal temperature and concentration of carbon dioxide in concrete, thereby contributing to the prediction of long-term neutralization of concrete and the research related to measures for neutralization of concrete.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.11a
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• pp.242-243
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• 2021

The concrete inside the steel tube of CFT columns enables them to have great strength and ductility. CFT columns are also excellent in fire-resistance because explosive heat upon a fire can be contained in the tube by the concrete debris. However, the studies to evaluate the residual strength of CFT columns after a fire have not been conducted enough. The studies to evaluate the residual strength of CFT columns after a fire are indispensable because it is the barometer of the damage of composite columns caused by a fire and the degree of repair and reinforcement work for the columns after a fire. Accordingly, the purpose of this study is to evaluate the deterioration of load capacity and structural behavior of square CFT columns with the same shapes and boundary conditions before and after a fire. The study also evaluates the influential factors of the CFT columns reinforced to secure the residual strength after a fire.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.

• The Journal of Sustainable Design and Educational Environment Research
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• v.23 no.2
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• pp.11-22
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• 2024

Rapid urbanization has led to the decline of local communities in old city centers and the deterioration of school facilities, resulting in an increased need for school complexes. School complexes are currently growing, with various policies being proposed. However, integrating additional facilities into the existing infrastructure in schools can lead to a range of issues. To mitigate these issues, additional traffic flow paths and complex integration strategies are required. This study categorizes school complex projects into various types based on themes and examines the relationships between each type and community revitalization factors. Through this analysis, the study recommends desirable directions for the implementation of elementary school complex projects to revitalize local communities.