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The deployment of vaccines across the US provides significant defense against serious illness and death from COVID-19. Over 70% of vaccine-eligible Americans are at least partially vaccinated, but there are pockets of the population that are under-vaccinated, such as in rural areas and some demographic groups (e.g. age, race, ethnicity). These unvaccinated pockets are extremely susceptible to the Delta variant, exacerbating the healthcare crisis and increasing the risk of new variants. In this paper, we describe a data-driven model that provides real-time support to Virginia public health officials by recommending mobile vaccination site placement in order to target under-vaccinated populations.more »Free, publicly-accessible full text available December 19, 2022
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While formal coursework remains one of the most common strategies for developing ethics knowledge and competence among engineering students, ethical situations also surface in many other settings. In our own research on engineering student perceptions of ethics and social responsibility, we found that many engineering interns and co-ops reported encountering ethical issues or dilemmas in the workplace. To further illuminate such encounters, this paper aims to: 1) identify and describe real-world ethical issues encountered by engineering students in workplace settings, and 2) investigate what students learned from these experiences. We address these objectives by reporting select results from an ongoingmore »Free, publicly-accessible full text available July 26, 2022
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Free, publicly-accessible full text available August 17, 2022
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Free, publicly-accessible full text available August 1, 2022
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Sosnovsky, S. ; Brusilovsky, P ; Baraniuk, R. G. ; Lan, A. S. (Ed.)As students read textbooks, they often highlight the material they deem to be most important. We analyze students’ highlights to predict their subsequent performance on quiz questions. Past research in this area has encoded highlights in terms of where the highlights appear in the stream of text—a positional representation. In this work, we construct a semantic representation based on a state-of-the-art deep-learning sentence embedding technique (SBERT) that captures the content-based similarity between quiz questions and highlighted (as well as non-highlighted) sentences in the text. We construct regression models that include latent variables for student skill level and question difficulty andmore »Free, publicly-accessible full text available July 1, 2022
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Multi- and hyperspectral imaging modalities encompass a growing number of spectral techniques that find many applications in geospatial, biomedical and machine vision fields. The rapidly increasing number of applications requires a convenient easy-to-navigate software that can be used by new and experienced users to analyze data, develop, apply, and deploy novel algorithms. Herein, we present our platform, IDCube that performs essential operations in hyperspectral data analysis to realize the full potential of spectral imaging. The strength of the software lies in its interactive features that enable the users to optimize parameters and obtain visual input for the user. The entiremore »Free, publicly-accessible full text available July 19, 2022
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Hui, Dafeng (Ed.)Abstract Aims Determining the ecological consequences of interactions between slow changes in long-term climate means and amplified variability in climate is an important research frontier in plant ecology. We combined the recent approach of climate sensitivity functions with a revised hydrological ‘bucket model’ to improve predictions on how plant species will respond to changes in the mean and variance of groundwater resources. Methods We leveraged spatiotemporal variation in long-term datasets of riparian vegetation cover and groundwater levels to build the first groundwater sensitivity functions for common plant species of dryland riparian corridors. Our results demonstrate the value of this approachmore »