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본 연구는 교통약자가 새로운 경로를 쉽게 찾을 수 있도록 경로추천 방법을 설계하고 구현하는 것이 목적이다. 이를 위해 교통약자가 이용하는 대중교통 수단과 이동편의시설, 통행시간, 대기시간, 환승 등의 이동불편요소를 이용하여 이동불편지수를 산정하였다. 산정된 불편지수를 이용하여 교통약자 유형별 최적경로를 추천하는 방법을 제시하였다. 서울 내 교통약자가 많이 거주하는 기점과 통행수요가 높은 종점을 추출하여 제시한 방법을 적용하였다. 적용 결과, 이동편의시설과 이동불편요소를 고려하여 교통약자 유형별로 이동 가능한 경로를 추천할 수 있음을 확인하였다. 본 연구의 결과는 향후 교통약자를 위한 경로정보 제공 서비스 구축에 활용할 수 있을 것으로 기대된다.

This paper presents an in-kernel, hardware-based control-flow integrity (CFI) protection, called PAL, that utilizes ARM's Pointer Authentication (PA). It provides three important benefits over commercial, state-of-the-art PA-based CFIs like iOS's: 1) enhancing CFI precision via automated refinement techniques, 2) addressing hindsight problems of PA for in kernel uses such as preemptive hijacking and brute-forcing attacks, and 3) assuring the algorithmic or implementation correctness via post validation. PAL achieves these goals in an OS-agnostic manner, so could be applied to commodity OSes like Linux and FreeBSD. The precision of the CFI protection can be adjusted for better performance or improved for better security with minimal engineering efforts if a user opts in to. Our evaluation shows that PAL incurs negligible performance overhead: e.g.

Calculated DDI counts between individual COVID-19 drugs and other prescription drugs for indications.

Informed by positionalities theories and narrative inquiry, this dissertation study explored how positionalities of immigrant social studies teachers in New York City influenced their interpretations of citizenship and their instructions of citizenship education. To do so, I used interviews, participants’ photographs and activity-works, and a self-reflexive researcher journal as aspects of my data-generating and data-gathering methods. My interpretations suggested that immigrant teachers experienced subjugation and discrimination as well as a sense of vulnerability due to their lack of legal citizenship, along with their minoritized racial/ethnic/linguistic/religious status in current racist, U.S.-centric, and nationalist regimes. However, instead of being passive recipients of such sociopolitical forces, these teachers took agency and created their own ways to actively influence, change, and subvert their minoritized subject positions through their transnational form of activities and attachment to their home country as well as the affinity, commitments, and sense of belonging they forged in local school communities in the United States. The complicated positionalities of these immigrant teachers further allowed them to imagine and practice multiple and alternative concepts of citizenship education that are more relevant to their students from minoritized backgrounds. By complicating essential, static, and fixed notions of immigrant teachers’ experiences and challenging dominant and normative modes of juridical notions of and national belonging in citizenship discourses through these immigrant teachers’ narrativized experiences, this study offers implications for social studies educators, citizenship scholarship, and teacher education policies and practices.

The analysis of remote sensing data depends on sensor specifications that provide accurate and consistent measurements. However, it is not easy to establish confidence and consistency in data that are analyzed by different sensors using various radiometric scales. For this reason, the cross-calibration method is used to calibrate remote sensing data with reference image data. In this study, we used an airborne hyperspectral image in order to calibrate a multispectral image. We presented an automatic cross-calibration method to calibrate a multispectral image using hyperspectral data and spectral mixture analysis. The spectral characteristics of the multispectral image were adjusted by linear regression analysis. Optimal endmember sets between two images were estimated by spectral mixture analysis for the linear regression analysis, and bands of hyperspectral image were aggregated based on the spectral response function of the two images. The results were evaluated by comparing the Root Mean Square Error (RMSE), the Spectral Angle Mapper (SAM), and average percentage differences. The results of this study showed that the proposed method corrected the spectral information in the multispectral data by using hyperspectral data, and its performance was similar to the manual cross-calibration. The proposed method demonstrated the possibility of automatic cross-calibration based on spectral mixture analysis.

Tissue damage caused by diseases and injuries requires regeneration to recover proper tissue function. Traditional treatments using autologous tissue have not yet met the need for tissue regeneration. Tissue engineering has emerged as an alternative strategy for tissue regeneration. It utilizes biomaterials that mimic native cellular microenvironments and provide environmental cues to induce specific cell responses, including differentiation. Biochemical and biophysical cues modulate cell behavior and direct construction of specific types of tissue. Thus, this work describes production and characterization of protein-engineered microenvironments that provide biochemical and biophysical cues to modulate stem cell differentiation.