Architecture Commons^™

Michael’S Mouth, Peter Olshavsky

Architecture Program: Faculty Scholarly and Creative Activity

“Michael’s Mouth” examines the virtuoso performance of small mouth sounds (“um,” “ah,” etc.) in MOS’ 2006 video, Alternate Ending 1: The Glimmering Noise. In this performance, “Michael” deftly uses non-words to advance a non-discursive argument about architecture as a form of attention in the post-critical imaginary.

Moral Distress Among Clinicians Working In Us Safety Net Practices During The Covid-19 Pandemic: A Mixed Methods Study, Donald E. Pathman, Jeffrey Sonis, Thomas E. Rauner, Kristina Alton, Anna S. Headlee, Jerry N. Harrison

Architecture Program: Faculty Scholarly and Creative Activity

Objective To explore the causes and levels of moral distress experienced by clinicians caring for the low-income patients of safety net practices in the USA during the COVID-19 pandemic.

Design Cross-sectional survey in late 2020, employing quantitative and qualitative analyses. Setting Safety net practices in 20 US states. Participants 2073 survey respondents (45.8% response rate) in primary care, dental and behavioural health disciplines working in safety net practices and participating in state and national education loan repayment programmes.

Measures Ordinally scaled degree of moral distress experienced during the pandemic, and open-ended response descriptions of issues that caused most moral distress. …

Deep Learning In Urban Analysis For Health, David Newton

Architecture Program: Faculty Scholarly and Creative Activity

The application of deep learning to urban health analysis is in its early stages, but offers new and promising capabilities in using large image-based datasets to better understand the built environment and its effects on human health. This chapter will introduce and explore some of these capabilities, providing the allied design fields with a roadmap of this emerging area of research, its potentials, and current challenges. The chapter begins with a brief overview of existing research related to urban morphology and health, in which precedent work using traditional methods as well as deep learning are introduced. Next, research is presented …

From Cropland To Cropped Field: A Robust Algorithm For National-Scale Mapping By Fusing Time Series Of Sentinel-1 And Sentinel-2, Bingwen Qiu, Duoduo Lin, Chongcheng Chen, Peng Yang, Zhenghong Tang, Zhenong Jin, Zhiyan Ye, Xiao Zhu, Mingjie Duan, Hangyu Huang, Zhiyuan Zhao, Weiming Xu, Zuoqi Chen

Architecture Program: Faculty Scholarly and Creative Activity

Detailed and updated maps of actively cropped fields on a national scale are vital for global food security. Unfortunately, this information is not provided in existing land cover datasets, especially lacking in smallholder farmer systems. Mapping national-scale cropped fields remains challenging due to the spectral confusion with abandoned vegetated land, and their high heterogeneity over large areas. This study proposed a large-area mapping framework for automatically identifying actively cropped fields by fusing Vegetation-Soil-Pigment indices and Synthetic-aperture radar (SAR) time-series images (VSPS). Three temporal indicators were proposed and highlighted cropped fields by consistently higher values due to cropping activities. The proposed …