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Most small and rural communities in the United States are shrinking. This population loss is often accompanied by economic and social upheaval—job losses, out migration of young people, school closures, reductions in local services, and deteriorating physical infrastructure. Because design firms cluster in metropolitan areas and most rural commissions are for private clients, architects are largely absent from these places. The AIA Framework for Design Excellence calls for the professional community to enable more sustainable, resilient, and inclusive environments, yet rural places pose a challenge because they remain a strikingly underserved market for architectural services. How can this vision for Design Excellence extend its reach into places where new construction is rare, and architects are not present to learn from and develop relationships with potential clients?This paper presents an overview of an interdisciplinary research project at Iowa State University funded by a $1.5 million grant from the National Science Foundation. The research begins with this question: why do people in some rural towns perceive their quality of life to be increasing even when the population continues to shrink? Using twenty years of survey data about quality of life, the team identified small rural communities in Iowa where the typical association of population loss with community decline did not appear to hold true. Through interviews, site visits, spatial analysis, and data analysis using machine learning and other methods, the team is working to better understand what influences people’s perceptions of quality of life. Understanding more about these unexpectedly resilient communities requires conversations and building trust in places where few outsiders ever visit. Examples of projects in towns working with the research team include adaptive reuse of closed schools and other abandoned properties; improved recreational spaces and parks; and repurposing underused commercial properties. 

Precision swine production can benefit from autonomous, noninvasive, and affordable devices that conduct frequent checks on the well-being status of pigs. Here, we present a remote monitoring tool for the objective measurement of some behavioral indicators that may help in assessing the health and welfare status—namely, posture, gait, vocalization, and external temperature. The multiparameter electronic sensor board is characterized by laboratory measurements and by animal tests. Relevant behavioral health indicators are discussed for implementing machine learning algorithms and decision support tools to detect animal lameness, lethargy, pain, injury, and distress. The roadmap for technology adoption is also discussed, along with challenges and the path forward. The presented technology can potentially lead to efficient management of farm animals, targeted focus on sick animals, medical cost savings, and less use of antibiotics. 

Purpose This study creates a COVID-19 susceptibility scale at the county level, describes its components, and then assesses the health and socioeconomic resiliency of susceptible places across the rural-urban continuum. Methods Factor analysis grouped 11 indicators into 7 distinct susceptibility factors for 3,079 counties in the conterminous United States. Unconditional mean differences are assessed using a multivariate general linear model. Data from 2018 are primarily taken from the US Census Bureau and CDC. Results About 33% of rural counties are highly susceptible to COVID-19, driven by older and health-compromised populations, and care facilities for the elderly. Major vulnerabilities in rural counties include fewer physicians, lack of mental health services, higher disability, and more uninsured. Poor Internet access limits telemedicine. Lack of social capital and social services may hinder local pandemic recovery. Meat processing facilities drive risk in micropolitan counties. Although metropolitan counties are less susceptible due to healthier and younger populations, about 6% are at risk due to community spread from dense populations. Metropolitan vulnerabilities include minorities at higher health and diabetes risk, language barriers, being a transportation hub that helps spread infection, and acute housing distress. Conclusions There is an immediate need to know specific types of susceptibilities and vulnerabilities ahead of time to allow local and state health officials to plan and allocate resources accordingly. In rural areas it is essential to shelter-in-place vulnerable populations, whereas in large metropolitan areas general closure orders are needed to stop community spread. Pandemic response plans should address vulnerabilities.