2027465

2020

The broader impact/commercial potential of this I-Corps project is the development of a novel, near-field, chemical imaging microscope probe based on a plasmonic fiber-tip assembly. This Fiber Tip-Enhanced Raman Spectroscope (F-TERS) probe is designed to provide the chemical composition of a sample surface while taking a nanoscale surface morphology image. F-TERS enables automation of nanoscale chemical imaging, which could move the technology from strictly research labs to industrial production settings. F-TERS can operate in virtually any gaseous and liquid environment, enabling new avenues in data collection for sample analysis in most real-world environments. The probe microscope market accounts for approximately $200 million and has been limited by measurement complexity and lack of chemical sensitivity. An optimized F-TERS probe could remove these limitations, creating an opportunity for growth within the microscopy market space. Development and commercialization of F-TERS would make nanoscale chemical imaging commercially viable for use in various markets such as the semiconductor, catalysis, and pharmaceutical industries.This I-Corps project is based on the development of fiber-coupled, nanoscale, chemical imaging spectroscopy probe. The probe uniquely integrates the plasmonic optical fiber and the most advanced Raman spectroscopy techniques for an imaging and spectroscopy instrument that can be used in a variety of commercial applications. The probe can be added to any existing nanoscale scanning probe-based microscope system (SPMs). Through F-TERS, an added dimension of chemical detection can be acquired by existing probe microscopes, substantially increasing their application and inherent value. Current methods of nanoscale chemical sensing require specific technical expertise, lengthy microscopy operational time, and subsequently, high cost. F-TERS will not require highly technical expertise but be usable by technicians who normally operate scientific instruments, greatly reducing the time and cost. The new probe assembly eliminates the need for laser alignment, improves the signal to noise ratio, lowers the required operating skill, and broadens the material compatibility. The self-contained design of F-TERS also allows it to function in most aqueous and gaseous environments with little or no sample preparation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027506

2020

This project will gather time-sensitive data needed to investigate the impact of the rapid shift to online instruction due to COVID-19. The project focuses on students in a project-based first-year engineering design course. Although it is effective in slowing viral infections, the sudden change to online instruction will also have a significant impact on undergraduate student experiences and learning. Successful online courses typically are developed over months or years, with substantial support from instructional designers or other specialists at the institution. In this case, the development of online courses had to be accomplished within weeks, and institutions did not have the capacity to scale assistance to all faculty as they converted their in-person courses to online courses. Courses that include hands-on experiences, such as design courses, are a particularly thorny challenge to effectively deliver online. High-quality instruction is critical in first-year courses, where students begin to form their identities as engineers and develop confidence in their engineering abilities. Understanding the effects that the rapid shift to online courses has on student experiences in project-based engineering courses could fundamentally change the way administrators, educators, and industry prepare for and adapt to the unique needs of first-year students. The findings of this work will provide insights that can guide informed decisions about the future of engineering education. Three research questions will guide the investigations: 1. How do the self-efficacy beliefs and identity of students evolve during this transitional period? 2. How do the self-efficacy beliefs and the experiences of instructors evolve during this transitional period? 3. Are the results of the first two questions conditioned upon individual differences or the use of mediating technologies? To answer these questions, the project will use a mixed methods approach that features qualitative interviews, surveys, data mining from learning management systems, and natural language processing. The research plan will be broken into two phases: 1) in-situ data collection and analysis; and 2) analysis of the collected data. The analysis will enable the research team to explore the implications of this sudden shift on future course offerings, instructor behaviors and beliefs, and student persistence, particularly the retention of first-year engineering students from underrepresented groups. It can also provide insights about how to improve online versions of laboratory, design, and other courses that require students to do hands-on work. This RAPID award is made by the Improving Undergraduate STEM Education program in the Division of Undergraduate Education (Education and Human Resources Directorate), using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027486

2021

Efforts focused on diversity, equity, and inclusion (DEI) and ethics are often siloed in engineering. While generally pursued as separate lines of investigation, we hypothesize that the aims, objectives, and goals pertaining to ethics and DEI often overlap. By investigating this potential overlap, we hypothesize that we can help improve overall efforts at promoting DEI and ethics in engineering. Our primary research objective is to synthesize intersections between ethics and DEI among engineering academic and workforce communities. In this study, we begin with a systematic literature review that explores potential overlap in literature in ethics and DEI. Second, we will study how engineering academics view (consciously and subconsciously) ethics and DEI as related. Finally, we will study how industrial practitioners view (consciously and subconsciously) the potential overlap between ethics and DEI. Collectively, this study will enable us to compare how literature, academics, and practitioners view ethics and DEI as related. We will use findings to generate curricular and workforce training efforts to better integrate ethics and DEI in engineering. This study will benefit society by promoting the formation of engineers who can engage with different values and perspectives in ethical ways.Despite various models, initiatives, and pockets of innovation by scholars and programs, we have not realized widespread changes in the diversification of the engineering workforce. We theorize that one barrier to change is the disjuncture between lines of scholarship from engineering education researchers in the intersecting spaces of DEI and engineering ethics. This study seeks to find ways for these communities to support one another by making explicit hidden structural issues that mask the intersections between ethics and DEI in the context of engineering. This study is comprised of three phases, addressing the following respective research questions: (1) How are engineering ethics and DEI related based on theoretical and empirical understandings of affective and cognitive development of students and practitioners within these communities?; (2) How are engineering ethics and DEI related based on mental models elicited from academics active in these two areas of research and scholarship?; and (3) How are engineering ethics and DEI related based on mental models elicited from a diverse cross-section of industrial practitioners? To address RQ1, we will use systematic literature review procedures to synthesize peer-reviewed scholarship on approaches to, and outcomes of, interventions centered around ethics and DEI. To address RQ2 and RQ3, academics (Phase 2) and industrial practitioners (Phase 3) will respond to an ethics/DEI challenge in multiple formats, including graphically, textually, and verbally. We will critically analyze the literature and mental models via a discourse analysis approach, guided by seven building tasks identified by Gee (significance, practices, identities, relationships, politics, connections, and sign systems). We will triangulate Phase 1, 2, and 3 findings to identify how discourses vary across the academic and industrial contexts. This triangulation will enable us to generate actionable modalities for supporting educational efforts aimed at the intersection of ethics and DEI both in curricular and workforce contexts. We will adopt an activist-oriented approach to disseminate findings to the research community and professional organizations through multiple mechanisms. This will directly benefit society by facilitating the professional formation of engineers who are more ethically adept and capable of engaging with difference.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027518

2020

The goal of this project is to create a software infrastructure that will help scientists investigate the risk of the spread of COVID-19 and analyze future epidemics in crowded locations using real-time public webcam videos and location based services (LBS) data. It is motivated by the observation that COVID-19 clusters often arise at sites involving high densities of people. Current strategies suggest coarse scale interventions to prevent this, such as cancellation of activities, which incur substantial economic and social costs. More detailed fine scaled analysis of the movement and interaction patterns of people at crowded locations can suggest interventions, such as changes to crowd management procedures and the design of built environments, that yield social distance without being as disruptive to human activities and the economy. The field of pedestrian dynamics provides mathematical models that can generate such detailed insight. However, these models need data on human behavior, which varies significantly with context and culture. This project will leverage novel data streams, such as public webcams and location based services, to inform the pedestrian dynamics model. Relevant data, models, and software will be made available to benefit other researchers working in this domain, subject to privacy restrictions. The project team will also perform outreach to decision makers so that the scientific insights yield actionable policies contributing to public health. The net result will be critical scientific insight that can generate a transformative impact on the response to the COVID-19 pandemic, including a possible second wave, so that it protects public health while minimizing adverse effects from the interventions.We will accomplish the above work through the following methods and innovations. LBS data can identify crowded locations at a scale of tens of meters and help screen for potential risk by analyzing the long range movement of individuals there. Worldwide video streams can yield finer-grained details of social closeness and other behavioral patterns desirable for accurate modeling. On the other hand, the videos may not be available for potentially high risk locations, nor can they directly answer “what-if” questions. Videos from contexts similar to the one being modeled will be used to calibrate pedestrian dynamics model parameters, such as walking speeds. Then the trajectories of individual pedestrians will be simulated in the target locations to estimate social closeness. An infection transmission model will be applied to these trajectories to yield estimates of infection spread. This will result in a novel methodology to include diverse real time data into pedestrian dynamics models so that they can quickly and accurately capture human movement patterns in new and evolving situations. The cyberinfrastructure will automatically discover real-time video streams on the Internet and analyze them to determine the pedestrian density, movements, and social distances. The pedestrian dynamics model will be reformulated from the current force-based definition to one that uses pedestrian density and individual speed, both of which can be measured effectively through video analysis. The revised model will be used to produce scientific insight to inform policies, such as steps to mitigate localized outbreaks of COVID-19 and for the systematic reopening, potential re-closing, and permanent changes to economic and social activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027521

2020

The Spatiotemporal Innovation IUCRC develops novel spatiotemporal analytical tools to enable applications of national and global significance. In response to the COVID-19 crisis, Harvard University and George Mason University, university sites within this IUCRC, propose this collaborative project to collect and share COVID related data in near real time, conduct spatiotemporal analytics, and mine socioeconomic and environmental knowledge to facilitate decision support systems in response to the pandemic. This project will build a unique cloud-based platform composed of a data collection subsystem for collecting global, high quality COVID-19-related data; spatiotemporal analytics tools for analyzing the disease evolution and socioeconomic patterns; and, modeling tools for assessing medical supplies and logistics. Through web access services, the platform will provide capabilities for easy access to the data collected as well as access to the developed spatiotemporal analytical and modeling tools. Such capabilities will facilitate quick production of data-driven decision support systems for community preparedness. This project has secured participation of 50+ international researchers in developing the proposed platform. These researchers will help collect and validate data, analyze how policies influence the outbreaks, how the Earth environment is impacted, and how to balance reopening of the economy and controlling the spreading of the disease in the U.S. based on experiences from Asia and Europe. Over 200 undergraduate volunteers, including many from underrepresented groups, are already involved in this project through Harvard’s Coronavirus Visualization Team efforts. Data, information, and knowledge accumulated in this project have been, and will continue to be, archived long term in a comprehensive gateway (covid-19.stcenter.net). Such data include spatiotemporal distribution of confirmed cases, relevant social, economic and natural information from different resources, such as authoritative reports, news releases, Earth observation, and social media. Software and tools developed are posted on GitHub for open access. Sustained online collaboration is being conducted to produce replicable research using spatiotemporal analyses to mine patterns and relations between COVID-19 and social and natural factors for community response and preparedness.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027532

2020

There are numerous forms of flight ranging from the natural flapping of insect wings to engineered multi-rotor helicopters. Among the diversity of systems for producing flight forces, the flying snake embodies a highly unexpected and non-intuitive solution for aerial locomotion. With a cylindrical body, the snake has no extendable surfaces to create or control flight forces. Despite these limitations, the Asian arboreal species known as ‘flying’ snakes possess a surprisingly sophisticated ability to glide. These snakes jump from trees, flatten their body, and undulate in the air in a complex three-dimensional pattern to produce aerial locomotion. Most surprisingly, the snakes can actively maneuver in the air, capable of turning in mid-air under their own volition. Understanding how flying snakes achieve such feats is the first step toward duplicating this behavior in engineered devices, which could significantly advance design of robots in complex environments, with important applications to surveillance, search-and-rescue, and disaster monitoring. The aerial interaction physics of flying snakes - the strong coupling between the translational and rotational degrees of freedom of the snake as an articulated body - is largely unknown. This project will test the hypothesis that translational-rotational coupling is achieved through feedback between self-deformations (driven by undulation) and unsteady fluid mechanics. The research will use a combination of animal observations, experimental fluid mechanics, and computational fluid dynamics to reveal the fluid mechanics of deforming articulated bodies, of which the flying snake (genus Chrysopelea) is the prime example. The application of adaptive mesh refinement-based immersed boundary method to study fluid flows produced by gliding snakes will enable more efficient investigations on other complex fluids problems with dynamically moving objects across a wide range of Reynolds numbers. The proposed experimental and computational framework can potentially re-define the form and function of locomotion in fluid media for aerial and underwater robotic systems with enhanced mobility. The project involves a broad participation plan that will benefit a diverse range of groups. The principal investigators will engage under-represented students through programmatic connections to regional HBCUs, for summer undergraduate research as well as recruiting of graduate research assistants, at the three collaborating universities. Flying snakes excite the imagination of both students and the public, and the results of the experiments and computations will be disseminated both professionally and publicly, to media outlets and also directly to the public through social media.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027529

2020

The goal of this project is to create a software infrastructure that will help scientists investigate the risk of the spread of COVID-19 and analyze future epidemics in crowded locations using real-time public webcam videos and location based services (LBS) data. It is motivated by the observation that COVID-19 clusters often arise at sites involving high densities of people. Current strategies suggest coarse scale interventions to prevent this, such as cancellation of activities, which incur substantial economic and social costs. More detailed fine scaled analysis of the movement and interaction patterns of people at crowded locations can suggest interventions, such as changes to crowd management procedures and the design of built environments, that yield social distance without being as disruptive to human activities and the economy. The field of pedestrian dynamics provides mathematical models that can generate such detailed insight. However, these models need data on human behavior, which varies significantly with context and culture. This project will leverage novel data streams, such as public webcams and location based services, to inform the pedestrian dynamics model. Relevant data, models, and software will be made available to benefit other researchers working in this domain, subject to privacy restrictions. The project team will also perform outreach to decision makers so that the scientific insights yield actionable policies contributing to public health. The net result will be critical scientific insight that can generate a transformative impact on the response to the COVID-19 pandemic, including a possible second wave, so that it protects public health while minimizing adverse effects from the interventions.We will accomplish the above work through the following methods and innovations. LBS data can identify crowded locations at a scale of tens of meters and help screen for potential risk by analyzing the long range movement of individuals there. Worldwide video streams can yield finer-grained details of social closeness and other behavioral patterns desirable for accurate modeling. On the other hand, the videos may not be available for potentially high risk locations, nor can they directly answer “what-if” questions. Videos from contexts similar to the one being modeled will be used to calibrate pedestrian dynamics model parameters, such as walking speeds. Then the trajectories of individual pedestrians will be simulated in the target locations to estimate social closeness. An infection transmission model will be applied to these trajectories to yield estimates of infection spread. This will result in a novel methodology to include diverse real time data into pedestrian dynamics models so that they can quickly and accurately capture human movement patterns in new and evolving situations. The cyberinfrastructure will automatically discover real-time video streams on the Internet and analyze them to determine the pedestrian density, movements, and social distances. The pedestrian dynamics model will be reformulated from the current force-based definition to one that uses pedestrian density and individual speed, both of which can be measured effectively through video analysis. The revised model will be used to produce scientific insight to inform policies, such as steps to mitigate localized outbreaks of COVID-19 and for the systematic reopening, potential re-closing, and permanent changes to economic and social activities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027556

2020

The novel coronavirus (COVID-19) has hit countries around the world hard and is likely to have both short-run and long-run impacts on health behaviors, social norms, and trust in government and other organizations. In the short run, governments and health organizations provide extensive information and recommend behavior to avoid contracting the disease and spreading it to others. This research involves surveys to figure out whether and to what extent people follow recommendations and change behavior. Because the research team has been following a sample of university students for several years, the team already knows a lot about them, and this facilitates an understanding of variation in compliance with recommendations. For example, risk-tolerance and trust in organizations are likely to matter. The team is exploring how people process information about the virus, and how that affects their beliefs about the risks to themselves and others. The researchers also are examining the impact of the COVID-19 outbreak on social norms, and how those change over time. The second wave of the study looks for longer run impacts. The results of this study will be useful in shaping future policies and communications about health risks, especially during epidemics and other health crises. The researchers make use of previous samples of subjects to test the impact of COVID-19 information and recommendations on behavior, social norms, trust in each other and institutions, and risk-tolerance. They have four areas of study. The first is how how people process “noisy” information in the context of COVID-19. Prior research by a team member has shown that some individuals tend to misunderstand such information to their benefit. The teams adapt the methodology and protocol of the prior work to examine how individuals interpret COVID-19 information, and how this affects their beliefs about their own vulnerabilities. Second, the team studies the impact of COVID-19 on norms of behavior, including those directly related to the virus (social distancing, hand-washing), as well as norms of trust, sharing and in-group favoritism that may be shifting or newly emerging in response to COVID19. Prior work by a team member developed a methodology for eliciting social norms, and has shown that norms evolve in response to social influence. Third, they explore the impact of COVID-19 on interpersonal trust and trust in institutions, which significantly impacts willingness to follow government and organizational recommendations. Prior work by team members used incentivized games and surveys to study trust and reciprocity in natural disaster settings. Finally, they look at risk perception and risk taking related to COVID-19. Using incentivized measures of risk tolerance, and survey measures of domain-specific risk perceptions and behavior, the team explores the relationship between risk aversion and behavior, but also how the advent of COVID-19 has changed preferences for risk-taking. In these ways prior knowledge about the subjects provides an opportunity to study the impact of a national health catastrophe on information processing, social norms, trust and reciprocity and risk-taking.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027571

2020

This project will conduct research to develop a rapid and powerful electronic probe that can detect biochemical attributes of COVID-19 from exhaled breath in real-time and on-demand. Our approach is an alternative and complementary solution to rRT-PCR for large-scale COVID-19 testing. The proposed sensor is a novel biohazard aerosol analyzer probe, which is based on phase interrogated ultra-sensitive microwave resonance. The probe can detect physicochemical attributes of human lung capacities and compositions of breath aerosols. We hypothesize that the compositions of the breath aerosols (water, virus, bacterial) will correlate to permittivity signatures of specific pulmonary diseases, which can be extracted using machine learning algorithms. The probe will separate sick from healthy individuals through a rapid and definitive test of an individual's breath. The proposal focuses on defining the theoretical and experimental sensitivity and selectivity of the probe and addresses the following: Is it possible to detect signatures from COVID-19 and other diseases from exhaled breath in real-time? Computer simulations will be employed to investigate the fundamental electromagnetic parameters of a prototype probe to determine the theoretical limit of detection. Probes will be fabricated and used to identify size distributions and chemical compositions of innocuous aerosols and those containing materials simulating viruses and respiratory tract secretions to detect COVID-19. Machine learning will be employed to analyze aerosol data and identify diseased individuals.Intellectual Merit: This work will advance the state-of-the-art of non-invasive point-of-care probes in the health care arena. The integration of machine learning data analysis with real-time and on-demand medical diagnostics is a novel contribution which will permit real-time evaluation of large-scale probe data and the concomitant detection of vectors of disease propagation.Broader Impacts: This work will inspire engineers to quickly advance the proposed strategy for identifying COVID-19 and other pulmonary disease signatures from human breath in real-time so that testing of human breath will soon become standard medical practice worldwide. The work is multidisciplinary, involving optics, electronics, chemistry, physics, virology, and machine learning. Testing has been identified as a scarce and essential resource. This research will permanently and dramatically enhance the containment of pandemics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027548

2020

The novel coronavirus (COVID-19) has hit countries around the world hard and is likely to have both short-run and long-run impacts on health behaviors, social norms, and trust in government and other organizations. In the short run, governments and health organizations provide extensive information and recommend behavior to avoid contracting the disease and spreading it to others. This research involves surveys to figure out whether and to what extent people follow recommendations and change behavior. Because the research team has been following a sample of university students for several years, the team already knows a lot about them, and this facilitates an understanding of variation in compliance with recommendations. For example, risk-tolerance and trust in organizations are likely to matter. The team is exploring how people process information about the virus, and how that affects their beliefs about the risks to themselves and others. The researchers also are examining the impact of the COVID-19 outbreak on social norms, and how those change over time. The second wave of the study looks for longer run impacts. The results of this study will be useful in shaping future policies and communications about health risks, especially during epidemics and other health crises. The researchers make use of previous samples of subjects to test the impact of COVID-19 information and recommendations on behavior, social norms, trust in each other and institutions, and risk-tolerance. They have four areas of study. The first is how people process “noisy” information in the context of COVID-19. Prior research by a team member has shown that some individuals tend to misunderstand such information to their benefit. The teams adapt the methodology and protocol of the prior work to examine how individuals interpret COVID-19 information, and how this affects their beliefs about their own vulnerabilities. Second, the team studies the impact of COVID-19 on norms of behavior, including those directly related to the virus (social distancing, hand-washing), as well as norms of trust, sharing and in-group favoritism that may be shifting or newly emerging in response to COVID19. Prior work by a team member developed a methodology for eliciting social norms, and has shown that norms evolve in response to social influence. Third, they explore the impact of COVID-19 on interpersonal trust and trust in institutions, which significantly impacts willingness to follow government and organizational recommendations. Prior work by team members used incentivized games and surveys to study trust and reciprocity in natural disaster settings. Finally, they look at risk perception and risk taking related to COVID-19. Using incentivized measures of risk tolerance, and survey measures of domain-specific risk perceptions and behavior, the team explores the relationship between risk aversion and behavior, but also how the advent of COVID-19 has changed preferences for risk-taking. In these ways prior knowledge about the subjects provides an opportunity to study the impact of a national health catastrophe on information processing, social norms, trust and reciprocity and risk-taking.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027590

2020

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) project is to develop a photocatalytic process to facilitate enhanced speed and efficiency for production of key intermediate chemicals for production of Remdesivir, a potential life-saving drug in the fight against COVID-19. Remdesivir has been identified as one of the most promising treatments for COVID-19; recently receiving fast-track approval for Phase III trials and compassionate use against this rapidly spreading and deadly disease. However, today Remdesivir is not an approved drug and is therefore not manufactured at scale. This project offers potential for accelerated production of Remdesivir by removing manufacturing bottlenecks of intermediates that threaten rapid scale-up and efficient manufacturing. Upon approval, process bottlenecks could inhibit mass-production, delay global availability and jeopardize millions of lives. An optimized production method for key intermediates is urgently needed to meet worldwide demand that will be critical in the race to control the pandemic. Potential problems with the current processes include costly and hazardous raw materials, low yields, expensive and specialized equipment required for harsh operating conditions and air- and water-free environments, and arduous purification and facility clean-up procedures. In addition, this project also provides a simplified, modular and faster approach for building libraries of derivative molecules to screen against COVID-19 and future viral threats.This STTR Phase I project proposes to develop an optimized manufacturing process for Remdesivir, one of the most promising COVID-19 therapeutic candidates identified to date, using photocatalysis, a powerful new chemical technology driven by light. Research objectives include using photocatalysis to develop a vastly superior synthesis route requiring fewer process steps, less toxic reagents and milder and safer reaction conditions. The new route will eliminate the need for specialized production equipment and procedures to maintain cryogenic temperatures and air- and water-free environments, will result in faster production times and overall higher yields, and will alleviate arduous purification and facility clean-up. The improved process will also facilitate installation of new molecular architectures and development of derivative molecules vital for efficacy screening against this and future viral threats. A second objective is to optimize the process for manufacturing at scale.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027620

2020

The coronavirus pandemic has influenced people’s marketplace behaviors. Panic buying has been evident among consumers all over the world, incurring substantial costs to the buyer, the society, and the marketplace. Hoarding and stockpiling creates shortage of supplies for necessities, depriving others who might be needing them the most in the situation (e.g., health care providers, older individuals), and artificially hikes price levels. For the panic buyer, this behavior creates inventory management problems. This research focuses on the consumer psychology of panic buying and how the media, policy makers, government officials and retailers can frame their communication with the general public at times of such unprecedented events, aiming to mitigate the psychological triggers of this behavior. The current research illuminates the psychological remedies of panic buying, and prescribes actions that may protect the supply chain, ensure equal distribution of necessities among the population, control price hikes of necessities, and extend consumers’ financial well-being in pandemic like situations. This research is two interrelated studies aimed at tackling the problem of panic buying owing to the COVID-19 pandemic. The first study explores the underlying psychological processes of panic buying during the ongoing crisis. Through an online survey, a U.S. national pool of 2750 respondents provides measures of relevant psychological factors and proneness to panic buying. The second study uses findings from the first study to develop communication frames and materials. The researcher then randomly assigns respondents to different conditions and evaluates the efficacy of the different conditions to reducing panic buying and hoarding.This project is jointly funded by DRMS and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027624

2020

This Rapid Response Research (RAPID) grant will develop the Models for Assessing Strategies for Hospitals (MASH) in Pandemics (MASH-Pandemics) Portal with requisite modeling capabilities urgently needed by hospitals and regions in responding to the COVID-19 pandemic. Important perishable, time-sensitive data and information to support this effort will be collected. MASH-Pandemics will build on previously developed sophisticated, detailed discrete-event simulation-based hospital capacity and capability analysis models of typical U.S. urban hospitals. This RAPID project will support the re-specification of these models, data collection, model runs, and results analysis, outcomes from which will aid hospital administrators and regions in making optimal operational changes and collaboration plans enabled through state and national emergency declarations in response to the COVID-19 outbreak. An online portal will be constructed on which details of the modeling capabilities, practical findings and recommendations, along with potential policy implications, for responding to the COVID-19 pandemic will be posted. Additionally, run requests from hospitals, hospital collaborations and geographical regions will be taken through the portal. This work will generate crucial synthetic data needed to develop quick recommendations and analyses in a period where time is of the essence. Key outputs will include, for example: potential for various modified operational strategies to benefit hospital performance and patient survival, hospital collaboration strategies to aid regional response, anticipating critical supply needs to mobilize and prioritize support from supply chains (or Federal response capabilities), and recommendations for effective implementation of capacity enhancement strategies (alternative standards of care, modified operations, demand management). The project will provide input to educational activities in the future, once the project is complete and the pandemic subsides. The focus of this work during the period of performance will be on providing, as quickly as possible, crucially needed recommendations to hospitals and regions based on results from runs of high-quality models. This RAPID award will advance mathematical modeling techniques for capturing critical hospital services during crises. It employs concepts of open queuing networks, discrete event simulation, stochastic modeling, transient system analysis, and statistical methods. The work will collect critical, perishable data, and will generate crucial synthetic data for rapid analysis and prediction urgently needed in this period of a global COVID-19 pandemic. With its quantitative approach, the project will enhance hospital readiness, capacity and capability, by identifying means for efficiently using severely limited, critical personnel and physical resources, the allocation of which will affect the survival of potentially thousands of lives and the safety of health care workers along with support staff. Findings from this effort will directly support hospitals at the front line, or regions in COVID-19 “hot spots,” by providing the opportunity to request runs and receive analyses of the effectiveness of COVID-19 response strategies and collaboration mechanisms. It is anticipated that the run requests will come in a variety of forms, requiring data collection, modeling work, investigation to capture stochastic processes with input distributions and parameters, and results analyses. The models can be quickly enhanced and mobilized, and initial findings and recommendations made public in only weeks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027654

2021

Integrated Digitized Biocollections (idigbio.org) at the University of Florida (UF), Florida State University (FSU), and Arizona State University (ASU) is the national coordinating body for the sustained effort to digitize, and make available online, the vast amount of information in the nation's biodiversity collections, which may contain up to 1 billion specimens. For biological specimens, information digitized include names of species, localities and dates of collection, digital photographs, sound, video, and 3-D models created from a variety of sources. This community digitization effort was catalyzed by NSF’s Advancing Digitization of Biodiversity Collections (ADBC) program via Thematic Collections Networks (TCNs), groups of institutions that digitize specimens to address a major research topic such as the relationship between agricultural crops and insects or the impacts of invasive species on natural ecosystems. iDigBio assists in coordinating activities of the TCNs by facilitating development of standards and workflows for digitization of specimens and related information, providing cyberinfrastructure resources to enable long-term preservation of digital data, promoting novel and traditional uses of collections data in research and outreach, and working with the collections community to plan for the long-term sustainability of the national effort and the resources it has produced. The availability of digitized information about specimens greatly enhances the ability to conduct research on biological diversity and to address some of the most fundamental questions in biology.Over the past ten years, the national effort to digitize information in the nation's biodiversity collections has been significantly advanced by the activities of iDigBio. Collaborations with data providers and users have been developed, goals and priorities defined, best practices related to digitization identified, and global collaborations with biodiversity data aggregators established. Cyberinfrastructure resources, including a national search portal, have been provided. These community-driven activities have led to improved digitization practices, increased involvement in digitization and training, and adoption of instruments and informatics tools that improve the efficiency and scalability of digitization and research workflows in all types of biodiversity collections. iDigBio works with staff in more than 926 collections in 317 institutions distributed across all 50 states. Communication among stakeholders to increase access to collections data has been established through workshops, webinars, and the use of social media. Since 2011, iDigBio has sponsored attendance of 16,768 participants from 1,034 institutions to 430 workshops, webinars, symposia, and events that targeted digitization-related topics. iDigBio has ingested 1,651 record sets containing 128 million specimen records and 41 million media records. All data ingested are indexed to enable queries and other types of index-based access. Searches for data can be done through a Web-based graphical interface or through programmatic APIs. Search and analytical tools enable users to mine diverse data such as taxonomy, location, images, traits, and vocalizations. During this award period, iDigBio to continue its successful strategies with an increasing emphasis on data improvement and use in research and outreach and to incorporate such rapidly developing technologies as artificial intelligence and machine learning in biodiversity data integration. iDigBio is recognized as an essential resource for information on biodiversity and digitization. As the scientific and societal benefits of validated collections data are realized, digitization will become a common and sustained practice in natural history collections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027658

2020

Course-based Undergraduate Research Experiences (CUREs) greatly expand opportunities for students to participate in authentic research early in their academic career. Research participation is linked to increased student persistence in STEM, especially for students from groups that are underrepresented in STEM. Thus, by increasing the number and diversity of students who have a research experience, CURES also broaden participation in STEM careers. CURES typically engage student teams in hands-on work in laboratories or field sites. With the outbreak of the COVID-19 virus, the national CURE education landscape quickly and dramatically changed to a fully “online” mode, with limited opportunity for advance planning. Thus, this situation presents a unique and urgent opportunity to explore how CUREs evolve in a new online environment, as well as whether they continue to achieve core CURE educational goals. It also allows for assessment of whether these rapid course changes have equitable outcomes for all students, including first-generation students, students on financial aid, and from different socioeconomic backgrounds. If CUREs are a solution for engaging large numbers of early-stage undergraduates in authentic STEM research, then the feasibility to translate CUREs online must be known. It is important to understand the structure of these newly online CUREs, in what situation they are effective, and who they benefit. The results of this project have the potential to expand understanding of CURE instructional approaches and outcomes, for both practitioners and researchers. Additionally, this unexpected shift to online coursework is an opportunity to engage broader higher education audiences in new thinking about course configuration, online effectiveness, and supports and barriers for online teaching.The purpose of this project is twofold: 1) capture and analyze how CURE course activities are rapidly translated into online formats; and 2) assess effects of course changes on students with different demographic profiles. It will provide early evidence to answer if, how, and why CURE benefits are realized through the different modality of online teaching. Importantly, this project will not make conclusions about the quality of online CUREs or online teaching overall; instead, it will explore which CURE activities can be readily delivered online, how they get delivered, how students respond, and how this new way of teaching changes/expands how instructors think about CUREs. The present project will use a mixed-methods design to track online implementation of CURE courses in two samples. The first sample will include a diverse set of local CUREs that span multiple STEM departments and have an array of course objectives/structures. This collection of CUREs will afford an in-depth, qualitative case study investigation that will capture and analyze instructors’ thinking, plans, and products, before, during, and after the shift from hands-on, laboratory-based CUREs to ones that are now abruptly online. The second sample will include CURE courses from a national network, which will allow us to explore more broadly and in a more quantitative way, how a large number of instructors transitioned to online. The project will also examine the resulting emotions, motivations, and experiences of students during this transition and how this semester’s student outcomes compare to those from prior years, through analysis of a historical student outcomes database. This approach will provide broad reach and comparison among a relatively homogenous set of CUREs, since all CURE instructors in this network are trained in the same research approach, have similar course objectives, and use similar materials. This RAPID award is made by the Improving Undergraduate STEM Education program in the Division of Undergraduate Education (Education and Human Resources Directorate), using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027677

2020

This EArly-concept Grant for Exploratory Research (EAGER) project will provide new methods for decision-support of healthcare operations in response to the COVID-19 pandemic. Because the coronavirus is highly contagious and results in severe disease in a relatively high percentage of those infected, healthcare resources, particularly hospital beds and essential personnel, are expected to be at or beyond capacity at the height of the epidemic. This project develops quantitative methods to estimate how interventions such as screening and surveillance via telemedicine and offsite testing and diagnosis can reduce the load on hospital personnel. The models developed are expected to improve the ability of hospital decision makers to predict equipment needs to ensure critical hospital personnel are protected and able to provide effective in-patient treatment.This EAGER award supports fundamental research in methods to integrate a susceptible-exposed-infected-recovered-infected (SEIRI) virus transmission model with a risk-averse sequential operational planning model for patient beds, staffing and personal protective equipment (PPE) in hospitals. The operational planning model comprises a stochastic optimization model that includes deployment of telemedicine for initial screening and surveillance, drive-through testing for diagnosis, and in-patient hospital care for those with severe disease. The project represents a tight collaboration between the PIs and Mayo Clinic Jacksonville (MCJ). The model parameters will be updated dynamically using MCJ data as part of a pilot project, and once tested, the decision-support system will be made available online for other hospitals in the nation and the world to use.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027678

2020

The outbreak of COVID-19 in the U.S. provides an important opportunity for researchers to improve flattening curve models which can be used to assess and even spatially optimize health care during a rapidly expanding pandemic. This Rapid Response Research (RAPID) project will take advantage of the large-scale availability of location-sensing devices and apps that produce big data on mobility patterns that can be used to better optimize the use of healthcare facilities. This research brings together rapidly unfolding health data with real-time data on mobility. We will examine how these two critical data resources can be linked to better inform policy, identify emerging hotspots, and target critical actions during a pandemic. This research will help public officials to better understand and adapt to changing conditions as a health emergency arises and expands.The spread of the “flattening curves” graphic was significant in promoting public understanding of the criticality of social distancing. These curves, however, were based on simulated data. This research will collect and examine mobility data and public health data to model flattening curves using real data. We combine big data from location-based apps and cellphones with Electronic Medical records from UMMS hospitals, including data on COVID-19 tests, and patient demographics and prognostics. New modeling approaches that quantitatively measure change in collective movement behaviors in response to the fast-evolving COVID-19 outbreak will be linked to hospital usage and capacity. The methods of this research will extend our knowledge of highly integrated systems, like transportation and health, and better prepare the public for future disasters.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027740

2020

Accurate measurements of temperature are critical for improving our understanding and modeling of flame structure and pollutant formation in flames. Modern computational models of both laminar and turbulent flames incorporate detailed models of chemical reaction kinetics and transport processes. The rates of chemical reactions in particular can exhibit strong temperature dependence. Consequently, accurate and validated methods for measuring flame temperatures are critical for rigorous comparison of numerical calculations of flame structure and pollutant formation with experiment. Coherent anti-Stokes Raman scattering (CARS) spectroscopy is widely considered to be the most accurate temperature measurement technique for flames but it is difficult to assess accurately the absolute temperature accuracy of the method, mostly because of the lack of other validated methods for measuring temperatures in excess of 2000 K. In this work a rigorous theoretical analysis of the accuracy of temperature measurements using the laser diagnostic method CARS spectroscopy will be performed. In addition, a new type of CARS experimental system will be developed to eliminate some factors which cause increased uncertainty in CARS temperature measurements. The goal of the project is to demonstrate temperature measurement accuracies of better than one per cent, which will enable rigorous evaluation of important aspects of computational flame models. Temperatures will be measured in both near-adiabatic Hencken burner flames and in non-premixed counterflow flames using an advanced high-spectral-resolution scanning CARS system. These measurements are enabled by the recent acquisition by our group of a high-power, single-frequency-mode, tunable titanium:sapphire continuous-wave laser system. The spectral width of the CARS laser beams will be approximately an order of magnitude less than the spectral width of the Raman lines for species such as N2 and H2. Consequently, we will be able to resolve the CARS spectral lines, minimizing the effects of the uncertainties their linewidths. The impact of the vibration-rotation interactions (the Herman-Wallis effect) and the effect of vibrational anharmonicity on the accuracy of temperatures extracted from N2 and H2 CARS spectra will be theoretically investigated. The accuracy of the high-spectral-resolution CARS temperature measurements will be validated in near-adiabatic Hencken burner flames. The validated CARS temperature measurements will then be performed in laminar, counterflow, non-premixed hydrocarbon/air flames, emphasizing measurements of flame structure near extinction.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027744

2020

COVID-19 has and is continuing to dramatically alter the lives of millions of Americans as businesses, schools, and many public places have closed around the country. Recommendations of public officials along with individual concerns and fears have fundamentally changed the pattern of daily routines as Americans have adopted the practices of social distancing, sheltering in place, and even self-quarantine. This Rapid Response Research (RAPID) project will improve our ability to assess and predict changes in mobility patterns under sudden disruptions caused by large-scale public health crises such as COVID-19. The specific focus will be to understand changes in mobility patterns and the complex and dynamic decision-making process shaping these changes during the unfolding events associated with this major public health crisis. The project will advance the national health, prosperity, and welfare by greatly improving the preparedness and responses of public agencies facing COVID-19 and future similar public health crises. It will also help understand and predict reduction, change, and recovery of human mobility patterns promoting the progress of science in human mobility and urban resilience, in alignment with the mission of NSF.The objectives of this RAPID project are to: (1) capture and ultimately predict spatiotemporal changes in the patterns of human mobility in response to the COVID-19 pandemic using social media data mining techniques; (2) perform high-frequency individual-level surveys via a smartphone app to understand motivational, decisional, and sentimental factors shaping changes in mobility patterns; and (3) explore conversion and convergence functions for high fidelity and high accuracy human mobility prediction. The intellectual merits of this research include: the discovery of unique mobility patterns emerging from this public health crises related to social distancing, sheltering, and self-quarantine practices; the unprecedented gathering of longitudinal evidence about the motivational, decisional and sentimental factors shaping mobility decisions; and the development of innovative algorithms of using a small representative sample for high-fidelity mobility prediction. The data and knowledge gained from the project will enhance future studies on urban mobility, travel demand and resource allocation modeling, and help policymakers assess the response and recovery of major urban metropolitan area facing a devastating disaster such as COVID-19. Project outcomes will be disseminated through the Boston Area Research Initiative (BARI), an inter-university partnership between Northeastern University and Harvard University, and through the MetroLab Network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

2027708

2020

COVID-19 has and is continuing to dramatically alter the lives of millions of Americans as businesses, schools, and many public places have closed around the country. Recommendations of public officials along with individual concerns and fears have fundamentally changed the pattern of daily routines as Americans have adopted the practices of social distancing, sheltering in place, and even self-quarantine. This Rapid Response Research (RAPID) project will improve our ability to assess and predict changes in mobility patterns under sudden disruptions caused by large-scale public health crises such as COVID-19. The specific focus will be to understand changes in mobility patterns and the complex and dynamic decision-making process shaping these changes during the unfolding events associated with this major public health crisis. The project will advance the national health, prosperity, and welfare by greatly improving the preparedness and responses of public agencies facing COVID-19 and future similar public health crises. It will also help understand and predict reduction, change, and recovery of human mobility patterns promoting the progress of science in human mobility and urban resilience, in alignment with the mission of NSF.The objectives of this RAPID project are to: (1) capture and ultimately predict spatiotemporal changes in the patterns of human mobility in response to the COVID-19 pandemic using social media data mining techniques; (2) perform high-frequency individual-level surveys via a smartphone app to understand motivational, decisional, and sentimental factors shaping changes in mobility patterns; and (3) explore conversion and convergence functions for high fidelity and high accuracy human mobility prediction. The intellectual merits of this research include: the discovery of unique mobility patterns emerging from this public health crises related to social distancing, sheltering, and self-quarantine practices; the unprecedented gathering of longitudinal evidence about the motivational, decisional and sentimental factors shaping mobility decisions; and the development of innovative algorithms of using a small representative sample for high-fidelity mobility prediction. The data and knowledge gained from the project will enhance future studies on urban mobility, travel demand and resource allocation modeling, and help policymakers assess the response and recovery of major urban metropolitan area facing a devastating disaster such as COVID-19. Project outcomes will be disseminated through the Boston Area Research Initiative (BARI), an inter-university partnership between Northeastern University and Harvard University, and through the MetroLab Network.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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