이강재 교수

To investigate the association between physical activity (including active travel modes) and environmental factors, much research has estimated contextual influences based on zones or areas delineated with buffer analysis. However, few studies to date have examined the effects of different buffer sizes on estimates of individuals’ dynamic exposures along their daily trips recorded as GPS trajectories. Thus, using a 7-day GPS dataset collected in the Chicago Regional Household Travel Inventory (C

Abstract Due to the advancement of tracking technology, a large quantity of movement data has been collected and analyzed in various research domains. In human mobility and physical activity (PA) research, GPS trajectories and the capabilities of geographic information systems (GIS) facilitate a better understanding of the associations between PA and various environmental factors taking individuals’ daily travels into account. PA research, however, needs to widen its focus from the intensity of

Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on stre

Abstract We study the properties of the loosely trapped surface (LTS) and the dynamically transversely trapping surface (DTTS) in Einstein–Maxwell systems. These concepts of surfaces were proposed by four of the present authors in order to characterize strong gravity regions. We prove the Penrose-like inequalities for the area of LTSs/DTTSs. Interestingly, although the naively expected upper bound for the area is that of the photon sphere of a Reissner–Nordström black hole with the same mass and

Previous studies have utilized regression models to investigate the impact of environmental factors on physical activity. However, such approaches are inadequate for data-driven analysis seeking to identify robust associations from the intricate and multi-variable interactions between physical activity and environmental factors. With the emergence of the concept of the exposome, which encompasses the totality of exposures, this paper explores machine learning models for predicting the percentage

Recently, human activities have expanded from outdoor spaces to indoor spaces since a lot of complex buildings were constructed over the world. Especially, visitors in a shopping mall would like to receive specific information of interest regarding various shopping-related activities as well as shopping itself. However, when it comes to providing the information, existing guide services have some drawbacks. Firstly, the existing services cannot provide visitors with the information of other stor

This study investigates the joint effect of air pollution and different types of green spaces (e.g. mixed forests) on stress levels in South Korea. Two periods were examined: before the COVID-19 pandemic (2017–2019) and during the COVID-19 pandemic (2020–2022). We used 16 total parameters for our Random Forest model. Stress was the dependent variable, and 15 other variables were independent parameters. Our focused independent parameters were PM10 and green spaces (forest types). Our findings sho

新汉语水平考试自2009年开始在海外组织实施以来，韩国考生的比例一直居高不下，并且至今仍呈逐年上升趋势。换言之，相比韩国国内其他汉语能力考试，新汉语水平考试目前占有极大优势，所以其对韩国的汉语教学所产生的影响该是不言而喻。本文即以韩国大、中学校在职汉语教育工作者及学习者为调查对象，就教、学双方如何看待新汉语水平考试的地位、体系等问题进行了问卷调查。研究结果表明，总体来看，调查对象对新汉语水平考试持否定态度。从具体数据来看，多数受访者认为新汉语水平考试的成绩具有十分的影响力，但同时也认为其分级体系存在问题。在新汉语水平考试给韩国的汉语教学所造成的影响等方面，回答亦以负面居多。另外，较旧HSK，多数认为对于韩国学习者，考试的区分度不尽理想。

Social restrictions, such as social distancing and self-isolation, imposed owing to the coronavirus disease-19 (COVID-19) pandemic have resulted in a decreased demand of commodities and manufactured products. However, the factors influencing sales in commercial districts in the pre- and post-COVID-19 periods have not yet been fully understood. Thus, this study uses machine learning techniques to identify the changes in important geographical factors among both periods that have affected sales in

Green space potentially promotes positive mental health in people. Consequently, the role of green spaces has become increasingly important because of the new coronavirus disease (COVID-19). While the association between green space and depression was explored during the COVID-19 pandemic, population data on this relationship before and after the pandemic are lacking. Furthermore, how different types of green space impact depression needs clarification. Thus, this study investigated the associat