University of Vermont & State Agricultural College

NSF Awards · FY 2024 · 2024-10

The current dominant model of agriculture achieves high yields due to the intense use of fertilizers and the adoption of mono-cultures. This dominant agricultural model, however, has contributed to social and environmental problems including biodiversity loss, increasing economic heterogeneity, and communities increasingly vulnerable to diseases, drought, floods, wildfires, and other natural disasters. Incentivizing the adoption of more sustainable agriculture practices would require a shift in farm rules. Most rules tend to disregard the environmental values that shape farmers' behavior (e.g., care and stewardship), and often focus solely on self-interested motivations (e.g., profit, productivity) at the expense of other values that may be important to farmers who hold a diversity of perspectives. Decision makers often struggle to address the diverse range of values that motivates farmers. For example, some farmers are mainly motivated to adopt sustainable agricultural practices because they see it as a market strategy, while others are driven by concerns with consumers’ health. This research project recognizes the different values that drive farmers’ behavior to identify the best instruments to incentivize sustainable agriculture. To identify the values that motivate the adoption of sustainable agricultural practices and how these values can be incorporated into rule design, the research team conducts case studies in the capital and one of the larger agricultural states. Brazil is leader in adopting rules that aim to incentivize new models of environmentally friendly agroecological production. This research is a multi-layered analysis through reviewing documents and interviewing stakeholders and farmers to ascertain how governance functions across institutional levels from national rules to local implementation and how values are translated across these levels. By identifying farmers' values and how they are aligned or not with rules, this research provides insight into how rules can better reflect farmers’ values and thus motivate more widespread adoption of sustainable agriculture practices. Although Brazil is the testbed, the findings are relevant to other nations with similar agro-ecosystems. 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.

NSF Awards · FY 2024 · 2024-10

Epidemics arise from interactions between pathogens and human hosts, where the pathogen influences human behavior and human behavior influences the spread of the pathogen. The models used to predict pathogen spread do not include the complexity of interactions between disease and human behavior but instead focus on biological processes and policy interventions. However, disease transmission depends on people’s behaviors, which are shaped by their perceptions of risk from the disease and from health interventions, as well as by the opinions and behaviors of the other people around them. This project will contribute to the development of mathematical epidemiological models that better represent the complexities of the human response to disease and that can be used to evaluate the relative impacts of public health policies on disease dynamics. The project will be focused on understanding respiratory diseases such as COVID-19, seasonal flu, and bird flu, but can be readily modified to be broadly applicable to other infectious diseases such as HIV or Ebola. The project will contribute to existing national COVID-19 and Flu Scenario Modeling Hubs that are working to better predict and understand the dynamics of infectious disease and to contribute to policy interventions. The Investigators will disseminate the results and foster connections with the disease modeling community through a workshop for public health professionals and will engage the public through production of educational music videos targeted at the broader community The complexity of human behavior is not well represented in epidemiological models, contributing to reduced skill and utility of model forecasts. While some epidemiological models represent human behavioral responses using a few static parameters, the Investigators will construct models of human behavior and policy processes that update dynamically to represent the dependence of human responses to the evolving state of the epidemic. Human cognition, social and policy responses will be represented using a system of differential equations linked with a traditional Susceptible-Exposed-Infected-Recovered epidemiological model using infectious respiratory diseases such as SARS-CoV-2 and H5N1 as model systems. Adoption of protective behaviors (vaccination, physical distancing) will be a function of risk perceptions (from disease and health interventions), health policies (lockdowns, vaccine mandates), and the behavior of other people (social norms). Policy interventions and adoption of protective behaviors mediate disease spread and impacts (infections and deaths) that influence human behavioral and policy responses. Mathematical novelty arises because cognition depends upon the history of infection, so the differential equations have past-dependence, generating differential integral equations. Model outputs will be used to analyze the sensitivity of and uncertainty in epidemic forecasts that arise from human risk perceptions, social influence, protective behaviors, and policy interventions. This project will advance the disease modeling community’s capability to analyze the interlinked dynamics of human social systems and infectious disease, increase the impact of social science on the disease modeling community, and will develop analysis methods for the complex and time-dependent interactions that arise from linkages of disease dynamics with social systems. This award is co-funded by the NSF Division of Mathematical Sciences (DMS) and the CDC Coronavirus and Other Respiratory Viruses Division (CORVD). 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.

NSF Awards · FY 2024 · 2024-10

Differential privacy is a state-of-the-art computing technique to protect individual data privacy in a dataset while allowing meaningful statistical analyses on the dataset. Currently, many barriers inhibit the implementation of differential privacy in real-world computing systems, one of which is the difficulty of communicating a specific system's actual privacy protection afforded by its differential privacy implementation. This project designs a standardized differential privacy certificate (DP certificate) to effectively communicate the privacy protection afforded by differential privacy to audiences with varying technical backgrounds, creates technical methods to ensure the contents of the DP certificate are accountable, and develops a software toolkit to help generate the DP certificate for a given system. The project's novelty is to create an effective and accountable way to communicate the actual privacy protection of differential privacy. The project's broader significance includes establishing a communication standard and facilitating the broader adoption of differential privacy. This project involves multi-area computing research. It includes a series of human subject research with multiple differential privacy stakeholder groups (e.g., differential privacy adopters, the general public, and differential privacy and standardization experts) to design a standardized DP certificate that can be understood by audiences with varying technical backgrounds, as well as formal methods research to create an accountability framework and relevant verification tools to verify key parameters on a DP certificate against the actual system. This project also develops a proof-of-concept software toolkit that helps differential privacy adopters create accountable DP certificates for their systems so that they can publicize their systems' privacy protection to the general public. The project outcomes establish a novel mechanism for effective and accountable communication of differential privacy, as well as widen the adoption and acceptance of differential privacy in real-world computing systems. This project's broader impacts include creating a differential privacy communication standard through the DP certificate, increasing public trust in differential privacy through the formal verification of privacy guarantee, and cultivating stakeholder appreciation for differential privacy through collaborative dissemination 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.

NSF Awards · FY 2024 · 2024-09

Flooding is rapidly becoming one of the most widely experienced, deadliest, and costly natural disasters threatening our economy, well-being, and security. While considerable effort has gone into improving flood forecasting models and mapping flood inundation hazards, mountainous settings pose unique challenges. Conditions that generate floods in mountain settings can be difficult to predict and model. Flood hazards in mountain settings are often characterized by erosional hazards that cascade through steep terrain and narrow stream and river corridors, with significant impacts on property, infrastructure, lives, and riverine ecosystems. To develop and employ actionable solutions to address the threat of mountain flooding, a deeper understanding is needed regarding the limits of existing flood forecasting services in complex mountain terrain, the needs of local communities experiencing catastrophic flooding, and the opportunities that nature-based solutions (NBS) afford for improving flood resiliency. Nature-based solutions (NBS) offer low-cost and strategic pathways to flood resilience by employing the services provided by intact forests, floodplains, wetlands, and river corridors as an alternative to engineered solutions to flood mitigation. This planning grant brings together Earth systems scientists, conservation organizations, government officials and planners, and other academic partners to consider the flood resiliency needs of communities, drawing upon examples in the Appalachian Mountains. The project objectives are to (1) assess community-based needs for improved flood hazard prediction, (2) explore the potential of new data sets and data driven modeling approaches to improve flood risk mapping, and (3) develop a pathway for the acceleration of science-based and community-engaged resiliency solutions. The objectives will be achieved through a series of knowledge-sharing webinars, field visits, participatory mapping exercises, and a grant-writing workshop. The overarching goal is to develop capacity for the integration of flood risk prediction science and NBS deployment that is responsive to community needs and builds resilience for highly vulnerable, rural communities in mountain regions. 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.

NSF Awards · FY 2024 · 2024-09

This Cyber-Physical Systems (CPS) project will fund research that investigates cognitive and cooperative sensing and imaging systems for rapid near real-time subsurface infrastructure monitoring and mapping. This research advances the frontier of subsurface sensing to a new paradigm enabling practical large-area surveys not possible by existing means. By significantly enhancing subsurface knowledge acquisition, the studied systems in this project will have far-reaching implications for maintenance and planning related to urban subsurface infrastructure, improving resilience, security, emergency response and urban renewal. The interdisciplinary nature of this collaborative research project broadens inter-institute and institute-community interactions. The research activities and outcomes will enhance and enrich existing STEM education curricula, CPS research efforts and summer programs for K-12 students, undergraduates, graduates and underrepresented groups in both Burlington, Vermont, and Chattanooga, Tennessee, leading to the development of a highly competitive and diverse STEM workforce for Internet of things, smart cities, public safety, and transportation industries. The goal and scope of this research project are to create faster and more accurate subsurface infrastructure sensing systems using teams of coordinated autonomous ground penetrating radars (GPRs) equipped with innovative and feedback-controlled cognitive slant sensing (CSS) capabilities. The research methods will involve collaborative and integrated development of hardware, communication networking, data acquisition and analytics, fundamental algorithms and models. The research approaches are to: 1) Build autonomous mobile GPR agents with slant scanning and edge-enhanced communication and computing. The CSS-GPRs can operate in both distributed and collective modes, with agents scanning individually or as a team in a scalable architecture; 2) Create synergistic multi-agent monostatic and multistatic teaming to map and construct 3-D images of subsurface infrastructure using novel slant imaging methods; 3) Assemble teams of autonomous GPRs with networking capabilities to enable adaptive switching between distributed and collective sensing modes; and 4) Validate with laboratory and field tests in challenging urban environments. The potential contribution of this research is advanced sensing systems that swiftly traverse designated terrains, employing data-driven adaptive methodologies to yield high-fidelity and scalable tomographic renderings of subsurface conditions and built infrastructure. 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.

NSF Awards · FY 2024 · 2024-09

The Earth contains an outer layer of continental crust which is important to society because it is a major repository of rocks and minerals that are natural resources used in construction, manufacturing, agriculture and energy. Thus, understanding how continental crust forms is vital to the success of our national economy. Geoscientists believe that continental crust is generated in magmatic arcs above active subduction zones, where the convergence of tectonic plates results in melting of the mantle (the layer below the crust) and/or the lowermost part of the crust. A key problem in understanding continental crust creation is that these processes occur at great depths within the Earth at 30-50 kilometers beneath the surface. Consequently, geoscientists have little direct information about the processes that occur in the roots of arcs except in rare locations where faulting has brought deep portions of the Earth to the surface. This conundrum leads to the central question of this project, what processes occur in the deepest parts of the crust that lead to the creation of new continental crust? This project addresses this problem by examining one of the few places on Earth where it is possible to directly observe the deep roots of continental crust above an ancient subduction zone, the Fiordland National Park located in the southwestern part of the South Island, New Zealand. These investigators, in collaboration with New Zealand researchers, will link structural, geochemical and isotopic data together to determine the processes and timescales involved in the creation of continental crust. The project will advance societal outcomes through an innovative post-M.S. internship that provides mentorship and training to a recently graduate M.S. student in scanning electron microscopy at California State University Northridge in Los Angeles County. This project will also provide graduate and undergraduate students with international field experiences and exposure to cutting-edge analytical research facilities that will aid in the development of a globally competitive STEM workforce.   The lower crust of arcs is commonly considered to be the engine room for continental crust creation; however, there is strong disagreement about how magmas diversify and how they are extracted from the lower crust to form middle- and upper-crustal batholiths. This project investigates whether magma diversification in the lower crust mainly occurs in deep-crustal, crystal-rich mush zones, where magma mixing, hybridization, fractional crystallization and extraction of trapped melts from crystal-melt reservoirs are the dominant processes that lead to geochemical diversification of magmas. The project involves linking field mapping and sampling, microstructural analysis using EBSD, bulk-rock and mineral geochemistry, and high-precision CA-ID-TIMS U-Pb zircon dating. This project focuses on testing three key questions specific to the Misty pluton, a large, superbly exposed lower-crustal intrusion that records igneous process from its base at ~40 km depth to its roof at ~25 km depth in Fiordland, New Zealand: 1) How do magmas diversify in the lower crust of arcs: are ‘MASH’ or ‘mush’ processes dominant in driving magmas to silicic compositions? 2) What mechanisms control the mobilization, segregation and extraction of melts in the lower crust? And 3) Over what timescales do magma diversification processes occur in the lower crust? The answers to these questions will provide insights into magma diversification, segregation and continental crust construction in the roots of a continental arc. This project involves a researchers and students from the California State University Northridge and the University of Vermont (an EPSCOR institution). The project promotes international collaborations with New Zealand researchers and public engagement through a series of ‘Fiord’ talks aimed at the general, non-science community in New Zealand to explore the geology and unique environment of Fiordland National Park (an UNESCO World Heritage Site). Talks will be recorded and uploaded to YouTube for broad dissemination to the public. 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.

NSF Awards · FY 2024 · 2024-08

On December 15, 2023, The National Academies of Sciences, Engineering and Medicine (NASEM) released a report entitled: “Foundational Research Gaps and Future Directions for Digital Twins” (“NASEM DT REPORT”). The purpose of this report was to bring structure to the burgeoning field of digital twins by providing a working definition and a series of research challenges that need to be addressed to allow this technology to fulfill its full potential. The concept of digital twins is compelling and has the potential to impact a broad range of domains. For instance, digital twins have either been proposed or are currently being developed for manufactured devices, buildings, cities, ecologies and the Earth as a whole. It is natural that the concept be applied to biology and medicine, as the most recognizable concept of a “twin” is that of identical human twins. The application of digital twins to biomedicine also follows existing trends of Personalized and Precision medicine, in short: “the right treatment for the right person at the right time.” Fulfilling the promise of biomedical digital twins will require multidisciplinary Team Science that brings together various experts from fields as diverse as medicine, computer science, engineering, biological research, advanced mathematics and ethics. The purpose of this conference, the “2024 Interagency Modeling and Analysis Group (IMAG)/Multiscale Modeling (MSM) Consortium Meeting: Setting up Teams for Biomedical Digital Twins,” is to do exactly this: bringing together such needed expertise in a series of teaming exercises to operationalize the findings of the NASEM DT REPORT in the context of biomedical digital twins. As part of outreach and training efforts to broaden the participation within this growing field, this workshop will provide support for both traditionally under-represented categories of senior researchers as well as junior researchers such as graduate students and postdoctoral researchers. Facilitating the development and deployment of biomedical digital twins requires operationalizing the findings and recommendations of the NASEM DT REPORT, which raises a series of specific and unique challenges in the biomedical domain. More specifically, there are numerous steps that need to be taken to convert the highly complex simulation models of biological processes developed by members of the MSM Consortium into biomedical digital twins that are compliant with the definition of digital twins presented in the NASEM DT REPORT. There are also identified challenges associated with these various steps. Some of these challenges can benefit from lessons learned in other domains that have developed digital twins while others will require the development of new techniques in the fields of statistics, computational mathematics and mathematical biology. This task will require multidisciplinary collaborations between mathematicians, computational researchers, experimental biologists and clinicians. This IMAG/MSM meeting will promote the concepts of Team Science to bring together experienced multiscale modeling researchers and experts from the mathematical, statistical, computational, experimental and clinical communities to form the multidisciplinary teams needed to operationalize the findings of the NASEM DT REPORT. The website for this meeting is at https://www.imagwiki.nibib.nih.gov/news-events/announcements/2024-imagmsm-meeting-september-30-october-2-2024, with the landing page for the Interagency Modeling and Analysis Group at https://www.imagwiki.nibib.nih.gov/. 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.

NSF Awards · FY 2024 · 2024-08

With the support of the Chemical Catalysis program in the Division of Chemistry, Professor Rory Waterman of the University of Vermont will study more efficient ways to form chemical bonds using metal compounds that are activated by light, a process called photocatalysis. The goal of this project is to investigate a new phenomenon in photocatalysis discovered in Prof. Waterman’s group, wherein the bonds to the metal photocatalyst are altered when exposed to light. Through a deeper understanding of this process, more efficient approaches to value-added products in the agrichemical, health, and consumer goods sectors will be developed. Specifically, photocatalytic approaches to forming bonds between the carbon and elements such as phosphorus, nitrogen, and sulfur, will be studied. By using photocatalysts, these compounds can be obtained with less waste and increased energy efficiency. Because the methodologies in the study are straightforward and many target metals are low or non-toxic, this project is an excellent way to include local high school students from underserved populations in Vermont as well as providing training for undergraduate and graduate students. The explosive popularity of photocatalysis has been driven by catalysts featuring rarified metals that engage in redox or radical reactions. In this study, the photocatalysts are excited in a charge transfer process that does not result in redox reactions and instead accelerates closed-shell, or non-radical, reactivity. Through spectroscopic analysis and computational modeling of known catalysts, the common features that determine activity and predictive power in discovering new catalysts can be uncovered. The starting hypothesis is that this photocatalysis involves bond-elongation in the excited state, and exploration of the mechanism will afford a greater understanding of what appears to be a general photochemical phenomenon among compounds with a metal-element bond in which the element has a lone pair of electrons. In the process of catalyst discovery, candidate catalysts using metals that are more sustainable and less toxic will be targeted for testing and study. Furthermore, efforts to extend this catalysis from preliminary examples of these reactions to the broadest set of substrates will be undertaken. These aims of greater understanding, discovery of new catalysts, and exploration of new reactivities will be used to prepare targets of known value to a range of sectors. This focus will provide participating students and trainees support in their assuming roles in the 21st century chemical/STEM workforce. 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.

NSF Awards · FY 2024 · 2024-08

With the support of the Chemical Synthesis (SYN) program in the Division of Chemistry, Professor Jose Madalengoitia of the University of Vermont will develop variations of an important chemical reaction, namely the allyl cyanamide rearrangement, which affords highly reactive intermediates en route to the preparation of guanidine compounds. The guanidine moiety is present in many drugs and bioactive natural products. In addition, guanidine compounds have also been found to catalyze many organic reactions. The methods and concepts developed in these studies have the potential to broadly contribute to the synthesis of complex, nitrogen-rich guanidine compounds such as those utilized in pharmaceutical chemistry, bioorganic chemistry, catalysis and materials science. A unifying concept of the project is the research group’s observation that orthogonally perturbed loose transition states for allyl cyanamide rearrangements correlate with a lower activation energy resulting in acceleration for the reaction rates for these and related reactions. Societal benefits will include the training of graduate students and undergraduates, both in experimental organic chemistry and computational chemistry. Furthermore, high school students recruited through project SEED will be recruited into the laboratory to perform research during the summers. Research in the Madalengoitia lab will focus on four projects that aim to develop and exploit the versatility of the allyl cyanamide rearrangement. The first project takes advantage of the ready availability of chiral pool amines and alcohols to synthesize enantioenriched allyl cyanamide rearrangement precursors, which after chirality transfer rearrangement and trapping by amines will afford more complex enantioenriched guanidines. The second project will exploit the orthogonal perturbation model to develop diastereoselective allyl cyanamide rearrangements by lowering the transition state energy of one diastereomeric transition state (through orthogonal perturbation) over another, leading to the selective formation of one diastereomer over the other. The third project investigates coupling the allyl cyanamide reaction to a zwitterionic, dynamically diastereoselective 1,3-diaza-Claisen rearrangement. The result of these cascade reactions will increase the structural complexity of the guanidine products and generate them with stereochemical control. The fourth project will develop the allenyl cyanamide rearrangement as a means of accessing vinyl carbodiimides that can react in situ with imines through a 4+2 cycloaddition to afford cyclic guanidines. To further highlight the versatility and applicability of these reactions, the four projects will be applied to synthesize advanced intermediates toward the synthesis of guanidine natural products. 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.

NSF Awards · FY 2024 · 2024-08

Climate change is affecting communities across the United States. It is causing water shortages for drinking and farming, and it also makes it hard to keep water clean and protect aquatic life. These issues impact everyone, regardless of financial status. They are particularly harsh on communities without proper infrastructure to prepare for, adapt to, and recover from water management problems, leading to economic losses and the loss of historic homelands. Extreme conditions can also cause harmful substances to spread beyond already contaminated areas. This project focuses on the Lakota Tribe in South Dakota, the Navajo Tribe in New Mexico, and flood-prone farming communities in Vermont. These groups are experiencing more frequent and intense droughts, wildfires, and floods. To address these challenges, we need to create intelligent and affordable systems to monitor water quantity and quality, using Indigenous knowledge and easy-to-use sensors to detect and measure pollutants in real-time. This collaboration, called Advancing Quality and Climate-Resilient Water Management with Community Partnerships and Enhanced Sensor Network (AQUA-CLIME), aims to help the Lakota Tribe, the Navajo Tribe, and Vermont farmers manage water quality and quantity issues caused by climate change. AQUA-CLIME will engage research experts, practicing professionals, and Indigenous communities from the University of Vermont and Norwich University in Vermont, South Dakota School of Mines and Technology and Oglala Lakota College in South Dakota, and New Mexico State University and Navajo Technical University in New Mexico. AQUA-CLIME will create a climate change research network involving people, equipment, and technology. It will enable smooth cooperation among Native American communities, farmers, students, teachers, industry groups, state agencies, and nonprofits. The project will also support career development for about 350 people, including 25 faculty researchers, 12 graduate students, 25 undergraduates, and 300 middle and high school students. The technical outcomes from this project would reveal underlying knowledge gaps about climate change impacts on water quality and quantity facing Indigenous tribes and agricultural communities in the three participating jurisdictions and broadly applicable to the U.S. The convergence research infrastructure guided by Indigenous knowledge is expected to yield fundamental insights to address data knowledge gaps and generate timely, actionable information for contaminants identification, quantification, mitigation, and communication. Outcomes from the work would include: (i) an integrated data science framework for monitoring contaminants under diverse climate change scenarios; (ii) a spatially distributed network of affordable, printable sensors and surrogate sensors for monitoring contaminants in watershed; (iii) data fusion methods for analyzing complex interactions among climate change scenarios and contaminant source/sink dynamics; (iv) strategies for integrating sensor networks for effective and equitable management of water resources; (v) functionalization strategies for obtaining smaller and yet smarter microsensors. The project will build community-academic partnerships that will contribute to a climate-resilient water management solutions for securing water quality and quantity, develop a future-ready expert workforce, and positively impact the socioeconomic status of disadvantaged communities in the three jurisdictions. This project is funded by the EPSCoR Research Infrastructure Improvement-Focused EPSCoR Collaborations (RII-FEC) program. The RII-FEC program builds inter-jurisdictional collaborative teams of EPSCoR investigators in focus areas consistent with the NSF Strategic Plan. RII-FEC projects include researchers from at least two EPSCoR eligible jurisdictions with complementary expertise and resources necessary to address challenges, which neither party could address as well or as rapidly independently. 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.

NSF Awards · FY 2024 · 2024-08

Viral infections by the Highly Pathogenic Avian Influenza virus (HPAI) A/H5N1 have historically affected wild birds with costly outbreaks periodically spreading in poultry flocks. With the emergence of HPAI clade 2.3.4.4b, new avian species and, even more concerning, terrestrial mammal infections have been reported, including an outbreak in dairy cattle, thereby creating an unprecedented and urgent health risk. The Food and Drug Administration reported that samples of pasteurized milk taken from grocery stores in the United States have tested positive for H5N1. Dairy farmers are key stakeholders, as their biosecurity strategies can minimize risks to the food supply. To do so, however, they need clear and actionable instructional messages. As the situation evolves dynamically and a potential crisis unfolds, the research team assesses and tests the efficacy of biosecurity recommendation messages. Specifically, the researchers (1) monitor real-time H5N1 crisis message recommendations in both social and traditional media to identify crisis constraints and misinformation; (2) assess the perceptions of dairy farmers and other national and state stakeholders who are responsible for identifying effective biosecurity strategies for managing the risk; and (3) provide feedback and recommendations to practitioners based on principles of instructional risk communication as articulated in the IDEA model. The IDEA model’s primary assumption is that instructional risk and crisis messages are most effective when they include a balance of internalization (affective learning), explanation (cognitive learning), recommendations for action (behavioral learning), along with considerations of how the messages can best be distributed. The intention of the research is to produce findings generalizable to biosecurity threats beyond this particular avian influenza virus. 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.

NSF Awards · FY 2024 · 2024-07

Evidence-based policymaking, the use of scientific evidence to inform policy, has been promoted as an objective way to identify problems and proffer effective solutions. The use of science to inform policy is now regarded as critical for effective policymaking, while the absence of science in the policy process may open doors to poor policy choices, policy failures, and detrimental policy outcomes. Although interest in understanding the use of science by policymakers has been growing, most studies focus on health policy, and often only in high-income settings. Consequently, there is limited understanding of the influence of science beyond the health policy field, and there is a large gap in knowledge around evidence-informed policymaking in low- and middle-income settings where existing scholarship may have little applicability due to dramatic differences in policy contexts. This research examines the roles of scientific evidence in the policy process in a large low-income setting, with a comparative viewpoint across multiple government entities (health, but also environment, agriculture, and social policy). The researchers seek to understand (1) how use of scientific outputs varies across different federal government components; (2) under what conditions science is more likely to influence decision-making; and ultimately (3) how science is used to influence policy in a large, economically important setting. A mixed methods approach - incorporating data from surveys and interviews of policy makers and analysts, alongside an analysis of original policy documents - provides a robust assessment of how scientific evidence is used throughout the policy process. This study thus contributes to a growing body of scholarship on evidence-based policymaking and offers insights into how to support the effective use of science in the policy process – which is a priority for many scientists, but also for many for-profit, non-profit, and government stakeholders worldwide. 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.

NSF Awards · FY 2024 · 2024-07

Inauthentic accounts are commonly used by adversaries on online platforms to carry out fraudulent activities like false advertising, scams, and personal threats. These accounts appear to belong to real people, but actually portray fictitious personas and are controlled by miscreants through semi-automated means to deliver potentially harmful content. Promptly detecting inauthentic accounts and fraudulent content is important to keep online users safe and prevent harmful and possibly illegal activity to thrive. Existing approaches to flag potentially harmful content either rely on learning behavioral traits of inauthentic accounts or on identifying keywords that are commonly used in fraudulent content. Existing research has, however, shown that adversaries adapt both their behavior and the content they post over time, with the goal of avoiding being flagged. In this project, the research team aims to address this problem by combining the two approaches into an end-to-end automated analysis pipeline. The project is improving the state of the art of automated identification of fraudulent online material. First, the team is developing robust artificial intelligence techniques to identify narratives used by previously identified inauthentic online accounts. These techniques will leverage advances in large language models and multi-modal embeddings to identify content that is posted on multiple platforms, consisting not only of text but also of images and videos. Second, the team is developing machine learning techniques to identify the characteristics of narratives used by adversarial actors, with the goal of identifying future harmful narratives irrespective of the content being shared. Third, the investigators aim to use the identified narratives to flag new inauthentic accounts, and learn their behavioral patterns for more effective detection. Used in conjunction, these three methods will allow researchers to identify the changes in content and behavior of harmful online campaigns, allowing for a more robust identification than what is currently possible. 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.

NSF Awards · FY 2024 · 2024-07

As the scale of computing and wireless communication continues to grow, energy consumption from these applications has grown faster than the installation of new energy generation capacity. The parallel growth in the number of wireless devices has also led to congestion of the wireless frequency bands that information is sent over, reducing connection speeds in busy areas. The goal of this project is to investigate and benchmark a new material, alpha-In2Se3, to make wireless devices that can transmit and receive simultaneously, improving efficiency. This material can efficiently convert electrical to mechanical energy (and vice-versa) and its electrical properties can be tuned, allowing for electrical amplification of traveling mechanical vibrations. The project will study three things towards this goal: 1) What are the best methods for working with this relatively new material for this application? 2) How can properties of the material be improved through careful engineering of materials it interacts with? 3) How does the material compare to other materials that are being researched for similar applications? The work performed will highlight diverse applications of semiconductors to local students, integrating with parallel efforts at UVM to build a strong workforce for the national semiconductor industry. Recent work has highlighted the promise of acoustoelectric amplifiers for on-chip magnetic-free circulators. This work will study the potential of alpha-In2Se3, a ferroelectric semiconductor, for fabrication of these devices by: 1) Developing a thorough understanding of alpha-In2Se3 exfoliation techniques, allowing for rapid study of the material and its mechanical and electrical properties. 2) Maximizing electron mobility through heterostructure fabrication, improving acoustoelectric device performance. 3) Surveying acoustoelectric device structures utilizing In2Se3 through analytical calculation and finite element analysis, identifying optimal theoretical structures as well as experimental bottlenecks that must be addressed while comparing the material to alternative technologies and acoustoelectric amplifier heterostructures. The microfabrication processes created under this grant will be used to highlight the opportunity of micro-electro-mechanical-systems in the developing multidisciplinary semiconductor engineering and physics certificate at UVM, attracting and training a more diverse semiconductor workforce. 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.