Molten salts have gained significant attention as a potential medium for heat transfer or energy storage and as liquid nuclear fuel, owing to their superior thermal properties. Various fluoride- and chloride-based salts are being explored as potential liquid fuels for several types of molten salt reactors (MSRs). Among these, chloride-based salts have recently received attention in MSR development due to their high solubility in actinides, which has the potential to increase fuel burnup and reduce nuclear water production. Accurate knowledge of the thermal physical properties of molten salts, such as density, viscosity, thermal conductivity, and heat capacity, is critical for the design, licensing, and operation of MSRs. Various experimental techniques have been used to determine the thermal properties of molten salts, and more recently, computational methods such as molecular dynamics simulations have also been utilized to predict these properties. However, information on the thermal physical properties of salts containing actinides is still limited and unreliable. In this study, we analyzed the available thermal physical property database of chloride salts to develop accurate models and simulations that can predict the behavior of molten salts under various operating conditions. Furthermore, we conducted experiments to improve our understanding of the behavior of molten salts. The results of this study are expected to contribute to the development of safer and more efficient MSRs.

Purpose: The purpose of this study is to recognize the role and necessity of public data visualization through prior research, investigation, and data verification processes. In addition, this study intends to check what factors should be considered in order to visualize data on the mobile web. Through this process, by identifying the cognitive load affecting information visualization by type, as a result, I would like to propose an effective information visualization method to effectively deliver public data related to government policies. Research design, data and methodology: In this study, we analyzed the case of information visualization according to infographics, which has been widely used in the public field among various visualization methods. For this study, a questionnaire survey was conducted for young people in their 20s and 30s with the highest mobile usage rate. Results: Based on the results, IPA (Importance Performance Analysis) was performed to conduct cognitive load test tools for information visualization of public data and confirmed the implications for each type of infographics. Conclusions: As a result of research, in order to efficiently deliver public data on the mobile web, first, it is necessary to construct a visual screen that can be easily identified through clear data. Appropriate graphic elements can be used according to the type to make it easier for users to acquire and understand information. Second, it is necessary to provide useful content in visualizing information. Third, in order to efficiently transmit information and increase understanding of data, it is necessary to visualize information that can induce interest in data and form metaphors. Fourth, it is necessary to visualize information to reduce cognitive load in terms of physical and mental aspects in order to accommodate users' comfortable information. Fifth, in order to effectively deliver public data, it is necessary to compose contents and information that are easy for users to understand. This study examines effective information visualization methods to increase the communication effect of public data in response to changes in the data-based intelligent information society and suggests implications for each type considering cognitive loads to help future public institutions to communicate and accept information.

Purpose: In modern society, many urban problems are occurring, such as aging, hollowing out old city centers and polarization within cities. In this study, we intend to apply big data and machine learning methodologies to predict depression symptoms in the elderly population early on, thus contributing to solving the problem of elderly depression. Research design, data and methodology: Machine learning techniques used random forest and analyzed the correlation between CES-D10 and other variables, which are widely used worldwide, to estimate important variables. Dependent variables were set up as two variables that distinguish normal/depression from moderate/severe depression, and a total of 106 independent variables were included, including subjective health conditions, cognitive abilities, and daily life quality surveys, as well as the objective characteristics of the elderly as well as the subjective health, health, employment, household background, income, consumption, assets, subjective expectations, and quality of life surveys. Results: Studies have shown that satisfaction with residential areas and quality of life and cognitive ability scores have important effects in classifying elderly depression, satisfaction with living quality and economic conditions, and number of outpatient care in living areas and clinics have been important variables. In addition, the results of a random forest performance evaluation, the accuracy of classification model that classify whether elderly depression or not was 86.3%, the sensitivity 79.5%, and the specificity 93.3%. And the accuracy of classification model the degree of elderly depression was 86.1%, sensitivity 93.9% and specificity 74.7%. Conclusions: In this study, the important variables of the estimated predictive model were identified using the random forest technique and the study was conducted with a focus on the predictive performance itself. Although there are limitations in research, such as the lack of clear criteria for the classification of depression levels and the failure to reflect variables other than KLoSA data, it is expected that if additional variables are secured in the future and high-performance predictive models are estimated and utilized through various machine learning techniques, it will be able to consider ways to improve the quality of life of senior citizens through early detection of depression and thus help them make public policy decisions.