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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 »Our strategy uses fine-grained mobility data, along with US Census and vaccination uptake data, to identify locations that are most likely to be visited by unvaccinated individuals. We further extend our model to choose locations that maximize vaccine uptake among hesitant groups. We show that the top recommended sites vary substantially across some demographics, demonstrating the value of developing customized recommendation models that integrate fine-grained, heterogeneous data sources. In addition, we used a statistically equivalent Synthetic Population to study the effect of combined demographics (eg, people of a particular race and age), which is not possible using US Census data alone. We validate our recommendations by analyzing the success rates of deployed vaccine sites, and show that sites placed closer to our recommended areas administered higher numbers of doses. Our model is the first of its kind to consider evolving mobility patterns in real-time for suggesting placement strategies customized for different targeted demographic groups. Our results will be presented at IAAI-22, but given the critical nature of the pandemic, we offer this extended version of that paper for more timely consideration of our approach and to cover additional findings.« less
Free, publicly-accessible full text available December 19, 2022
Kim, D.; Howland, S. J.; Jesiek, B. K.(
, Proceedings of the 2021 American Society of Engineering Education Virtual Annual Conference)
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 »qualitative analysis of 33 interviews with undergraduate students in their fourth year of college. We more specifically present a series of illustrative cases drawn from four of the interviews, selected because the participants described specific work situations in considerable detail and the cases represent a wide variety of ethical concerns. The purpose for sharing these cases is threefold. First, we note some specific lessons that our subjects learned (or failed to learn) through the selected cases. Second, we argue that the workplace is a particularly rich setting for learning about professional ethics. Third, we make recommendations for better scaffolding and supporting student learning in workplace settings. We expect this paper will be of particular interest to engineering ethics scholars studying where and how students learn about ethics, instructors looking for ways to enhance and extend ethics learning, and students preparing for internship, co-op, and/or full-time job roles.« less
Free, publicly-accessible full text available July 26, 2022
Kim, D. Y.; Scott, T. R.; Mallick, D.; Mozer, M. C.(
, Proceedings of the Third International Workshop on Intelligent Textbooks (iTextbooks))
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 »augment the models with highlighting features. We find that highlighting features reliably boost model performance. We conduct experiments that validate models on held-out questions, students, and student-questions and find strong generalization for the latter two but not for held-out questions. Surprisingly, highlighting features improve models for questions at all levels of the Bloom taxonomy, from straightforward recall questions to inferential synthesis/evaluation/creation questions.« less
Free, publicly-accessible full text available July 1, 2022
Mishra, D; Hurbon, H; Wang, J; Wang, T; Du, T; Wu, Q; Kim, D; Cao, Q; Basir, S; Zhang, H; et al(
, Workshop on Hyperspectral Image and Signal Processing Evolution in Remote Sensing)
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 »software can be operated without any prior programming skills allowing interactive sessions of raw and processed data. IDCube Lite, a free version of the software described in the paper, has many benefits compared to existing packages and offers structural flexibility to discover new hidden features.« less
Free, publicly-accessible full text available July 19, 2022
Barber, S; Ibarguen, S; Sharp, C; Kim, D; Chang, Y; Ashcroft, J; Faltens, T; Reedy, A(
, Zone 1 Conference of the American Society for Engineering Education)
Steinberg, Kelly A; Eichhorst, Kim D; Rudgers, Jennifer A(
, Journal of Plant Ecology)
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 »to identifying which plant species will thrive (or fail) in an increasingly variable climate layered with declining groundwater stores. Important Findings Riparian plant species differed in sensitivity to both the mean and variance in groundwater levels. Rio Grande cottonwood (Populus deltoides ssp. wislizenii) cover was predicted to decline with greater inter-annual groundwater variance, while coyote willow (Salix exigua) and other native wetland species were predicted to benefit from greater year-to-year variance. No non-native species were sensitive to groundwater variance, but patterns for Russian olive (Elaeagnus angustifolia) predict declines under deeper mean groundwater tables. Warm air temperatures modulated groundwater sensitivity for cottonwood, which was more sensitive to variability in groundwater in years/sites with warmer maximum temperatures than in cool sites/periods. Cottonwood cover declined most with greater intra-annual coefficients of variation (CV) in groundwater, but was not significantly correlated with inter-annual CV, perhaps due to the short time series (16 years) relative to cottonwood lifespan. In contrast, non-native tamarisk (Tamarix chinensis) cover increased with both intra- and inter-annual CV in groundwater. Altogether, our results predict that changes in groundwater variability and mean will affect riparian plant communities through the differential sensitivities of individual plant species to mean versus variance in groundwater stores.« less
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