Effect of Trust in Metaverse on Usage Intention through Technology Readiness and Technology Acceptance Model

The fourth industrial revolution enhanced the development of information technology in all fields and opened up possibilities. A lot of attention is focused on the future possibilities opened up by the metaverse, the core of information technology. Metaverse will have a big impact on reality and the near future. Metaverse is a virtual world that fuses physical and digital reality. Various commerce such as healthcare, instruction, business, and land are foundation to utilize metaverse knowledge in their regular work. There is a series of processes in the stage where newly developed technology is introduced to general users. In order for a new technology to become a user-friendly technology, it is necessary to verify the technology. It can be said that it is hard to derive the operator\u27s usage intention in a state where user trust for new technology is not verified. In the metaverse environment, it is necessary to first verify the trust for new technologies. This study is expected to understand usage intention through the process of checking trust in metaverse, and to become basic data for the popularization of metaverse knowledge. The meaning of this research is to inspect the influence relationship of trust in metaverse on usage intention through Technology Readiness (TR) and Technology Acceptance Model (TAM). Statistical package (SPSS23.0) was used for basic numerical examination of the questionnaire. Hypothesis test was performed using the structural equation package Smart PLS 3.0. Discriminant validity and concentration validity of the questionnaire were verified. As parameters that trust in metaverse effects, TR and TAM were set. As factors constituting TR, it was separated into optimism, innovativeness, discomfort, and insecurity. The TAM is separated into perceived usefulness and perceived ease of use. The outcomes of the study are as follows. First, trust in metaverse had a significant effect on TR. Second, TR was partially adopted in the TAM. Innovativeness and perceived usefulness had no significant effect. Third, TAM significantly influences usage intention. Fourth, perceived ease of use did not significantly influence perceived usefulness

?????????????????? ??????????????? ????????? ????????? ????????? ?????????????????? ??????

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)Rapid urban growth has resulted in increases in the amount of impervious surfaces in cities, inducing changes in urban climates, which are generally warmer than their rural surroundings This phenomenon is known as the urban heat island (UHI) effect. For these reasons, the use of green areas has been increasing to reduce the heat island phenomenon in the city. However, many studies have analyzed the UHI intensity using land surface temperature (LST) since it is extremely difficult to accurately measure the surface air temperature for a wide range of urban spaces. Because the LST is a measurement of how hot the land is to the touch, it is different from the thermal condition in which people actually feel. Therefore, this study evaluates the cooling effect of green areas by estimating the air temperature using the Random Forest model, one of the machine learning techniques. 138 AWS temperature data were collected for Seoul city. Land surface temperature was calculated by LandSat 8 OLI image using Surface Energy Balance Algorithm (SEBAL). Also, vegetation, Urban, and Weather related variables were used for prediction. The vegetation variables are Nomalized Difference Vegetation Index (NDVI), Weighted Difference Vegetation Index (WDVI), Soil Adjusted Vegetation Index (SAVI) and Leaf Area Index (LAI). The variables related to the weather were Albedo, elevation, longitude, latitude, and Julian Day. Also, we used the 100m and 400m buffered area within the building area, the building coverage area and the average height of the buildings. In order to analyze the cooling effect of the green space, the temperature distribution was confirmed for each land use in Jung - gu and Jongno - gu in Seoul. As a result of the analysis, Random Forest prediction model showed high prediction performance( =0.69. RMSE=1.23). The temperature cooling effect of green areas was also confirmed, but the effect was insufficient.clos

Intermodal connectivity and its impacts on HSR ridership : Seoul Station and Yongsan Station, South Korea

textSouth Korea launched its first high-speed rail (HSR) system in 2004. The primary goal of developing the system was to serve the citizens with improved regional mobility. The government has also invested a large amount of capital in providing amenities and convenience to passengers for the purpose of increasing HSR ridership; improving intermodal connectivity is among the efforts taken by the government and related agencies Yet whether improved intermodal connectivity translates into increased HSR ridership remains under-documented and under-researched. . This professional report examines the question by focusing on two HSR stations in the South Koreea case: Seoul Station and Yongsan Station. This report first presents the basis information about Korean HSR and the stations. It then documents government programs pertaining to intermodal connectivity. For reference purposes, a number of international cases are also reviewed and presented. Lastly, the PR examines the relationship between intermodal connectivity and HSR ridership and offers policy recommendations aiming at increasing ridership and enhancing services.Community and Regional Plannin

Measurement and Simulation of Ship Under-Keel Clearance in Port Approach Channels

Abstract not availabl