Virginia Polytechnic Institute and State University

NSF Awards · FY 2024 · 2024-10

Next generation wireless communications will need to support heterogeneous devices with different capabilities on communications, computations, and power to deliver applications with various performance demands such as high data rate, low power consumption, and low latency. Massive multiple-input multiple output (MIMO) has been widely considered a compelling technology for achieving high capacity and high spectrum efficiency in the future wireless communication networks. To fully unleash the potential performance gains claimed by massive MIMO communication systems, it is of vital importance to have timely and accurate channel state information (CSI) at the transmitters, especially at the base station side. The main goal of this project is to explore a systematic approach that accelerates the CSI processing by orders of magnitude in massive MIMO communication systems. The project will lay a foundation to enhancing data rate and energy efficiency, spectral efficiency in the next-generation wireless communications. The research efforts associated with the project can have a significant impact on the lightweight artificial intelligence (AI) design for wireless communication systems, which will further improve many application domains, including beyond 5G wireless networks, autonomous machine-to-machine communications, vehicular networks, and Internet-of-Things. The outcomes of the project can foster the transition of our society into the intelligent wireless networking age, where wireless communication systems can provide seamless support to match many different wireless applications for massive network devices and support many services with high computation demands and quality of service needs. Moreover, the Principal Investigators are committed to integrating research and education by introducing emerging computing and lightweight AI in wireless communication systems into the current electrical and computer engineering curricula in the three participating universities. The project will also provide opportunities for students to learn, develop and apply advanced wireless communications, which they would not receive from a traditional B.S. or M.S. curriculum. Meeting the coherence time requirement in massive MIMO systems can be extremely difficult for CSI processing due to the complex traditional model as well as AI model development and inconsistent performance across environments. In this research project, theoretical analysis and performance evaluations will be obtained for novel algorithms designed for 1) optimization on the decompressed feature in the CSI reconstruction process, 2) simplifying the AI structures for multi-rate compression and reconstruction, and 3) autonomous CSI reconstruction performance evaluation and AI model update. The optimized features and simplified AI structures can significantly reduce the complexity in terms of floating point operations per second (FLOPs). Thus, the AI implementation can be accelerated by 1 to 2 orders of magnitude without losing reconstruction accuracy for timely CSI processing in massive MIMO communication systems. The systematic methodologies can be readily extended to facilitate many other applications that encounter the similar challenges and present similar needs on reducing latency and computation needs. Furthermore, this research project can greatly promote the understanding in AI-supported massive MIMO systems for better spectrum and power efficiency and will contribute fundamentally to the design of highly efficient machine-to-machine communications that require high level of autonomy. 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

This doctoral dissertation project studies the collection of non-timber forest products (NTFP) by refugee populations and their local hosts. Tree cover loss is a significant concern across refugee-hosting regions, and yet NTFPs are important sources of livelihood, cooking fuel, nutrition, and healthcare for both refugee and host communities. Models of optimal foraging theory predict that collectors of NTFPs aim to select high-value items located close to the home site while conserving as much energy as possible. This study advances current theories of human foraging by examining whether forced displacement impacts the ability of refugees to collect NTFPs according to these theoretical expectations when compared to nearby hosts amid factors such as land access, social relationships, and knowledge of local flora. Research on NTFP collection and use patterns in refugee contexts is critical for the development of culturally appropriate humanitarian policies and programs that support refugee and host communities in meeting their needs in a sustainable manner. Such policies and programs can reduce tensions over natural resources, contributing to regional and global security. The collection of non-timber forest products is a vital coping strategy for refugees and hosts to fill subsistence and livelihood gaps that are unmet by humanitarian organizations, especially in geographical settings which host the majority of global refugees. A novel contribution of this study is to quantify and compare NTFP collection patterns across groups of refugees and hosts. It also extends optimal foraging theory, a widely applied framework that is rooted in ecological principles, to a refugee context. Furthermore, this project is based on a participatory and community-engaged approach to collecting field-based data on the locations of NTFPs by refugees and local hosts, which NTFPs they collect, and how their patterns of NTFP collection compare to theoretical predictions. The results contribute to refined understandings of natural resource use in refugee settings, with critical social and environmental importance as refugee displacement intensifies worldwide. The project also contributes to the training and education of an early-career social scientists. 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

This project aims to serve the national interest by developing a new introduction to wireless communications course, centered on the principles of problem- and project-based learning (PBL/PjBL) and integrating advanced visualization tools. The US national security and economic competitiveness depend on leadership in, as well as the use of advancements in, wireless technologies and their applications. Achieving these ends thus requires recruitment and development of a well-prepared workforce of wireless communications/radio frequency (RF) engineers and other STEM professionals who understand wireless communications engineering and RF concepts. With the increasing popularity of specializations that inherently produce visual, observable, or tangible outcomes, such as robotics and autonomous vehicles, the number of students choosing to study wireless communications has steadily gone down, even though wireless is a key enabling technology for many of these applications. This project brings together experts from wireless communications, engineering education, visualization, mathematics, and mathematics education to rethink how we introduce wireless communications concepts to students. All the course materials developed as a part of this project, including the lecture plan, syllabus, lecture notes/slides, projects, and interactive simulations/visualizations, will be made publicly available. This approach will offer a template that can be adopted at other schools so that they do not have to go through the whole process of redesigning the course from scratch. A subset of the projects developed as a part of this course will also be adapted for outreach events targeting pre-collegiate and first-year students. Guided by the theoretical framework of experiential learning theory, this project plans to develop an interactive and visually engaging introduction to wireless communications resulting in the following innovations. First, is the redesign of a complete introductory course based on PBL/PjBL concepts that will change the way wireless principles are introduced to the students. Second, to ensure tight integration of PBL/PjBL, each learning objective of the course will be carefully mapped to the PBL/PjBL learning activities through a new “learning objective matrix” that will provide a systematic way of developing a detailed lesson plan with PBL/PjBL at its core. Third, storyboards will be integrated in the course to provide multiple potential workflow options that the students must navigate, as well as to provide a mind map to the instructors. Fourth, will be the development of the first complete suite of learning resources and projects (with a significant interactive and visualization component) for a complete course on wireless communications. Fifth, is the implementation and assessment of this course redesign that will address multiple engineering education research questions and will provide feedback for further refinements to this course. Finally, the project aims to close the gap between what academia teaches and what industry wants by closely aligning with topics of current interest to wireless industry, such as that of “digital twins,” which also advocates the use of visualization for designing and analyzing wireless systems. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. 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

Work-related musculoskeletal disorders (WMSDs), such as back pain, sprains and strains, continue to impose an economic burden to the United States, amounting to around $54 billion annually. These economic burdens are around compensation costs for the employer, lost wages for the employee, and decreased productivity. The rapid technological advances in artificial intelligence (AI) offers new opportunities for WMSD risk assessment by enabling automated analysis of WMSD risk factors. However, there are concerns regarding potential biases in AI, particularly across workforce demographics. This project aims to investigate these algorithmic biases to ensure the ethical design and deployment of responsible and ethical AI technologies for the benefit of employees and employers across the United States. This aligns with NSF's mission to promote the progress of science, and advance the national health, prosperity and welfare. This project seeks to: (1) obtain stakeholder perspectives (benefits and concerns) on AI-based ergonomic assessments; (2) identify sources of algorithmic biases in AI-based ergonomic assessments; (3) develop and evaluate methods to mitigate algorithmic biases; and (4) hold focus groups and sessions with workers to understand AI biases and effects on worker health and explainability. This project aims to bring about substantial contributions to advance worker health and safety by addressing critical issues related to AI bias and fairness. The work has potential benefits for the United States economy and society, through improving worker health and well-being and reducing employer compensation costs, and to enhance diversity in the workforce, especially for women and older workers. 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

Mobile Internet measurement is critical to network design, resource allocation, and troubleshooting network issues. However, sharing of mobile Internet measurement data can potentially compromise user privacy. Given the wide introduction of artificial intelligence to mobile Internet measurement and traffic analytics, there is an urgent need for data sharing solutions that provide explainability in terms of the trade-offs among data quality, utility and quantity. To close the gap, the objective of this project is to develop new methods to augment data with explainable data quality and utility, to access and share collected data in a privacy-preserving manner, and to collaboratively analyze Internet data with intelligence and autonomy. This collaborative project brings together investigators from University of Nebraska-Lincoln, Utah State University, and University of Wisconsin-Madison. It aims to lay a solid foundation for mobile Internet measurement with privacy preservation, collaborative and distributed intelligence, and autonomy. Methodologies and methods will be developed for quality-explainable data synthesis and augmentation; privacy-preserving data sharing; and collaborative and privacy-preserving analysis of Internet measurement data. Moreover, a mobile Internet traffic generator will be developed for evaluating the proposed methods. This project can significantly advance the prior research in Internet traffic analytics, quality-explainable and privacy-preserving data processing, mobile Internet traffic analytics, distributed artificial intelligence and machine learning algorithms, optimizations, modeling, simulations, and testbed experiments. The research efforts associated with this project will greatly advance the understandings of the critical issues in the next-generation mobile Internet measurement with distributed and collaborative intelligence to provide privacy-preserving data sharing and Internet traffic analytics. The outcomes of the project can potently foster the transition of our society into data sharing with privacy and intelligent era. Research and education will be integrated in this project by introducing emerging mobile Internet measurement and privacy-preserving data processing with advanced topics such as 6G wireless systems, data augmentation, artificial intelligence and machine learning models into the current curricula in the three collaborative institutions. The project website is hosted at: cns.unl.edu/imr-ppds. The collected data, simulation codes, and publication list will be published on the project website. Copies of technical reports and accepted manuscripts will also be published on the project website. The website will be maintained during the project years, and remain accessible for least 2 years after the completion of the project. 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

Quantum information science (QIS), which uses the laws of quantum physics to process and store information, is expected to broadly impact society through new developments in commerce, governance, privacy, employment, education, and other areas. However, a well-trained QIS workforce is necessary to make these advances. Unfortunately, QIS is a challenging, interdisciplinary field to learn. The goal of this project is to advance QIS education by using virtual reality (VR) and machine learning to adaptively address misconceptions about the field. The project will directly impact the education of approximately 120 undergraduate students learning QIS and has the potential to help transform how to motivate and prepare students for future quantum workforce positions. This project will leverage QubitVR, a VR application previously developed for learning foundational QIS concepts like superposition, measurement, and entanglement. As a first aim, the project will identify and predict QIS misconceptions by collecting data from a controlled, general-population study of QubitVR. This aim will include the development and validation of a new QIS Concept Introductory Test (QISCIT) for assessing learning outcomes. It will also involve labeling misconceptions in the collected data and the development and systematic evaluation of machine learning models based on VR tracking and input data for predicting when QubitVR learners are likely to have a misconception. As a second aim, the project will adaptively tutor QIS misconceptions by developing two intelligent tutoring versions of QubitVR: one that employs proactive conceptual scaffolds based on the machine learning models and one that employs reactive scaffolds based on conventional action-condition rules-based reasoning. This aim will involve one of few studies to directly compare machine learning-based and rules-based approaches to intelligent tutoring by comparing the two versions in a between-subject, general-population study. As a third aim, the project will ecologically validate the efficacy of QubitVR by collecting control, baseline, and adaptive tutoring data from undergraduate QIS courses in a longitudinal study. As a final aim, the project will result in the development of desktop and smartphone versions of QubitVR, which will be made openly available alongside the VR versions for broader educational impacts and to advance QIS education beyond the scope of this project. 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

Historically, water systems in urban communities have been thought about, regulated, and managed as three distinct sub-systems: drinking water, wastewater, and stormwater. Practitioners throughout the world are increasingly embracing the idea of integrated management of these three subsystems with the end goals of improving water quality, increasing water supply reliability, reducing freshwater withdrawal, and achieving energy and cost savings. However, widespread adoption of this integrated “One Water” vision will require a radical departure from the siloed way these systems are typically managed. This CIVIC project will foster a technology- and community-centered approach for managing cascading water quality risks in the Occoquan Reservoir, a source of drinking water for up to 1 million people in Northern Virginia. To accomplish this, we will work with local communities to identify concerns and potential solutions to the water quality challenges facing this critical water supply. We will then pilot test a new water quality modeling framework with our community partners. The modeling framework will serve as the centerpiece of a generalizable system-of-systems approach for managing emerging contaminants and other acute and chronic water quality challenges in One Water systems. Our vision for managing water quality risks in One Water systems is predicated on an ability to link upstream pollution sources to downstream water quality, ideally in real time. Models of pollutant fate and transport through reservoirs typically take the form of software packages that numerically solve momentum, energy and mass conservation equations, empirical models, machine learning approaches, or multi-model ensembles. The approach proposed here, transient transit time distribution (T-TTD) theory, takes an entirely different tack by tracking the flux and age distribution of water and pollutants moving into and out of a control volume drawn around the reservoir. By eliminating the need to describe within reservoir transport processes, T-TTD theory vastly simplifies model development, reduces computational requirements for real-time deployment, and opens the door to unbiased assessment of model structure and parameter inference. It is also strongly data driven and thus leverages the high-frequency flow and water quality monitoring data routinely collected in One Water systems. Co-production of this modeling framework will ensure that it is salient, credible, and legitimate for decision-making. The goal is to provide communities and practitioners with the actionable information they need to manage cascading water quality risks in more integrated and equitable ways, both now and under various population growth and climate change scenarios. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

Public support for scientific influence on policy increases when scientists are perceived as acting in the public’s best interests. Yet most U.S. adults are skeptical of scientists’ goodwill (e.g., commitment to public good). Goodwill is a key dimension of source credibility, a shortcut used by the public to interpret scientific findings and recommendations. Despite its key role, little is currently known about how goodwill emerges, varies and is contested across individuals and groups to motivate (or not) evidence-based action. This project will address this gap by identifying the key drivers of perceived technoscientific (TS) source goodwill and determining when and how technoscientific goodwill and communication shape responses to environmental messages among agricultural land operators (i.e., farmers, foresters, ranchers). Research findings will encourage efforts to incentivize evidence-based land stewardship that mitigates land-based climate emissions and maximizes the benefits of conservation science for people and the environment. The proposed CAREER project will identify an improved evidence-based model of the factors shaping public perceptions of technoscientific (TA) source credibility and further efforts to measure, test, and employ goodwill as a means of promoting effective public communication. This research uses latent semantic analysis and mixed-methods content analysis to identify and compare the socio-cultural beliefs undergirding agricultural land operators and TS sources’ mental models of source credibility. An online message experiment will be used to test the effects of goodwill signals and hedging on perceptions of TS source credibility and support for conservation practices on agricultural lands. Furthermore, this CAREER project integrates research and teaching with the development and evaluation of a novel educational curriculum that engages agricultural communities and prepares natural resource professionals to signal goodwill and recognize the cultural realities informing responses to evidence-based messages. As such, this project will provide a foundation for improved science communication training, more effective and evidence-based environmental messaging, and enhanced goodwill between agricultural communities and TS sources. 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

Despite being one of the key driving forces behind scientific discovery and playing a critical role for the well-being of our society as a whole, scientific data sharing remains an issue fraught with security and integrity risks. This is because certain restrictions on data usage (e.g., maintaining appropriate audit logs for the data modifications) must be enforced rigorously, which is difficult to accomplish with existing technologies in a seamless manner. The need to share scientific data while honoring usage restrictions, protecting data integrity, and capturing provenance for scientific repeatability requires development of new software tools that can support these capabilities across multiple organizations’ boundaries. The novel blockchain based data sharing system built as a part of this project may result in advancing the knowledge base on how to automatically balance the conflicting goals of security and integrity requirements and information sharing in the context of open scientific discovery process. This system in return can significantly increase volume and variety of data shared for research purposes across a broad range of disciplines, thereby directly addressing the NSF Big Idea on Harnessing the Data Revolution, by further advancing capabilities of modern cyberinfrastructure and fostering novel opportunities for interdisciplinary data-intensive research. Furthermore, by actively incorporating our research results into the curriculum development, at both undergraduate and graduate levels, we foster the next generation of interdisciplinary scientists in a new era for open data. To achieve the above stated goals, this project builds an innovative system where blockchain techniques are leveraged for assured scientific data sharing. First, the project develops a policy specification framework tailored for open science data sharing. Using this framework, scientists may easily specify any usage restriction (e.g., do not re-identify human subjects, do not modify data, notify us if the data is lost due to hacking). Later on, using the blockchain framework, these restrictions will be captured, and stored via smart contracts. One novel aspect of the proposed framework is that, using machine learning techniques, it can analyze the smart contract based provenance information to automatically detect potentially anomalous data modifications. To test the capabilities of our framework, the project uses a multi-sensor based data collection and data sharing project for understanding the impact of urban pollution as a case study. 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

This project focuses on improving access to Internet and digital services among rural tribal communities, which are historically some of the communities most underserved by Internet in the United States. Effectively using digital services to access critical health, educational, and other social services requires both access to affordable, reliable broadband infrastructure as well as the training and support to take advantage of that infrastructure. Recognizing the high cost and complexity of extending traditional, always available, Internet infrastructure to the hardest-to-reach, in this project we propose an alternative: dynamically reliable Internet access, a supplemental form of network infrastructure that provides predictable, reliable, but periodic Internet service to otherwise unserved locations at a fraction of the cost of traditional permanent Internet infrastructure. We couple this with outreach efforts to provide digital skills training on location to people served by this supplemental network service, thus ensuring newly-served populations can make effective use of this infrastructure. In this pilot project, we partner with the Nez Perce Tribe and Nez Perce Network Systems, a Tribal utility providing Internet service, to design a dynamically reliable cellular network and to extend their existing digital skills training and support program to provide on-site support with tribal elders where they reside. We will leverage the Nez Perce Tribe’s existing fixed network infrastructure to support this network, along with the Tribe’s extensive 2.5GHz radio frequency spectrum holdings. In doing so, we will explore what dimensions of reliability are relevant in the context of digital inclusion and expand the binary notion of "reliability" operationalized in contexts such as federal digital equity policy to a design space to allow network operators to match technical affordances of low-cost network infrastructure with user needs within their community. This project is in response to the Civic Innovation Challenge program’s Track B. Bridging the gap between essential resources and services & community needs and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. 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

The growing stress from spectrum shortages and the increasing demand for wireless applications are propelling spectrum management into its fourth era. The "Pay-As-You-Go and Cooperative Sharing" vision is poised to be a promising new paradigm for spectrum management in Spectrum Era 4. In this vision, despite cooperation among wireless users, the information they can share is limited to user/application or system/protocol-level parameters (e.g., spectrum requirements, interference tolerance levels, wireless standards, and waveform types). However, signal-level information, representing instantaneous transmission details of individual data packets (e.g., channel coefficients), cannot be shared in a timely manner across different wireless networks due to delays in cross-network information exchange. This project aims to fill this critical gap by investigating interference mitigation techniques for wireless devices in the absence of signal-level interference information. The research team will design learning-based approaches for individual radio devices to decode their data packets in the presence of unknown interference. The team will also integrate the proposed interference mitigation algorithms into 5G Open Radio Access Networks (O-RANs) and evaluate their performance in realistic scenarios through comprehensive experimentation. Moreover, the project will promote the participation of students in wireless communications research. It will also enhance pedagogical activities by developing new course materials based on the research findings. The research team will focus on three thrusts to enable transparent and concurrent spectrum utilization for heterogeneous wireless network systems by developing learning-based approaches capable of mitigating unknown interference. First, the team will design supervisory learning algorithms for interference mitigation by leveraging the reference symbols in physical-layer signal frames and the spatial degrees of freedoms provided by a radio device’s multiple antennas in sub-10GHz wireless systems, with the goal of enabling individual radio devices to decode data packets in the presence of unknown interference. Second, the team will design online-learning-based beamforming methods for interference mitigation in millimeter-wave (mmWave) systems, aiming to maximize transmission data rates despite interference with unknown signal-level features. Third, the team will integrate the proposed interference mitigation algorithms into a 5G O-RAN testbed and explore computational acceleration methods (e.g., using specialized hardware) to meet the real-time requirements. The proposed interference mitigation algorithms will be evaluated through comprehensive over-the-air experiments in realistic scenarios. 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

The rapid advancement and widespread deployment of generative artificial intelligence foundation models, particularly large language models, have also led to an escalation of risks. Despite the tremendous efforts put into their safety alignment, these models have been shown to be fragile, vulnerable to "jailbreaking" attacks against safeguards built into deployed models, and prone to systematic deterioration after custom fine-tuning. The goal of this project is to advance our understanding of the fundamental causes of safety issues and innovate more effective methodologies for ensuring safety, thereby enabling the trustworthy deployment of foundation models in diverse applications. The scientific advances resulting from this work will support the pressing national need for trustworthy artificial intelligence that benefits society at large. Furthermore, this project will impact a broader audience through the organization of workshops, innovation competitions, and the development of educational curricula. This project will pursue the following tasks, weaving safety measures throughout a model's lifecycle: (1) This project will conduct in-depth analyses of the model's behavior and contributing factors to identify the root causes of harmful outputs and develop targeted interventions. (2) This project will develop a comprehensive testing framework that subjects the model to diverse simulated threats, assessing its resilience, identifying vulnerabilities in human-like interactions, and developing effective countermeasures to enhance robustness. (3) This project will explore methods to integrate safety constraints into the model adaptation process and establish systems for continuous monitoring of the model's behavior in real-world applications to ensure the model remains aligned with desired behaviors, secure, and reliable as it is applied in new contexts. 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

This project aims to serve the national interest by mapping national patterns in the use of research-based instructional practices in post-secondary chemistry, mathematics, and physics courses five years after the disruptions due to the COVID-19 pandemic. In the Spring of 2019, a survey was sent to roughly 18,000 instructors of first-year mathematics, chemistry, and physics courses at nearly 1000 post-secondary institutions. That survey provided a comprehensive view of introductory science courses and instructors across the United States, with responses from nearly 4000 faculty from 660 U.S. colleges and universities. However, just one year later colleges and universities across the nation quickly shifted to online, emergency remote teaching in response to the COVID pandemic. The scale of instructional change during this time was both unprecedented and ubiquitous, with nearly every instructor teaching in the spring of 2020 required to try something new, and many needing to continue experimenting and revising their courses for the following semesters. This project will repeat the 2019 survey in order to characterize any lasting impact of the COVID pandemic on undergraduate science education and understand what this new instructional landscape may mean for change agents working to improve undergraduate science education through the uptake of research-based instructional practices. The goals of this project are to 1) understand the impact of the COVID pandemic on undergraduate science education as well as provide a current description of undergraduate science instruction, and 2) in consideration of any shifts following the COVID disruption to higher education, revise and update the research-based insights and recommendations for supporting and achieving instructional change in undergraduate STEM. To do so, the roughly 18,000 instructors will be re-surveyed. Some of the survey analyses will be conducted on the new responses alone, including multilevel modeling of the impact of malleable factors on instructors’ adoption of research-based instructional practices. Other analyses will incorporate the prior results for pre-post analysis to capture changes in the practices of both individuals and the disciplines in the aggregate. Where changes are observed, additional statistical tests and modeling will be used to identify the impact of emergency response teaching strategies on those shifts. These findings will be used by change agents (e.g., professional development organizations, instructional coaches) to better support undergraduate instructors in implementing research-based instructional strategies and by administrators (e.g., department chairs, course coordinators) in making resource allocations and policy decisions. These results will update the foundational knowledge base needed to support widespread pedagogical shifts toward the use of research-based instructional practices in post-secondary STEM education, impacting undergraduate students across the country. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Institutional and Community Transformation track, the program supports efforts to transform and improve STEM education across institutions of higher education and disciplinary communities. 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

Privacy is often perceived as an abstract concept by both internet users and software developers. When users are engaged in online activities, it is difficult for them to make informed decisions about their personal data due to the challenges they face in understanding and experiencing the privacy implications of their behaviors in advance. Similarly, many software developers lack the ability to comprehend how the data practices of their applications may impact user privacy and to implement proper data practices that conform to users’ privacy expectations. This project is tackling this problem by developing a new, empathy-based framework to enhance privacy education and design. The project team is using generative AI to create synthetic personas with AI-generated personal data. Using the personas, the team is designing, creating, and studying new interactive sandboxes and developer tools that allow individuals to empathize with these personas, leading to a more concrete and situated understanding of privacy. This understanding, in turn, fosters positive privacy-oriented behaviors among internet users and privacy-responsible software development practices among software developers. To enhance users’ privacy knowledge and developers’ privacy-responsible software development practices, the project is systematically studying the mechanisms and applications of empathy invocation in the context of privacy. The goal is to develop metrics, guidelines, and conceptual frameworks for empathy-based approaches that foster privacy and security in cyberspace. Using these findings, the project team is employing user-centered design methods to develop: 1) systems that invoke empathy to improve users’ privacy literacy and decision-making; and 2) empathy-based developer tools that support developers to proactively identify and address diverse privacy needs of users at the early stages of the development life cycle. These systems are deployed in outreach events to promote privacy literacy in under-resourced user and developer communities. Additionally, they are incorporated into college-level privacy literacy educational modules to support hands-on experiential learning. 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

Cells need a steady supply of RNAs and proteins to survive and perform all their functions. Yet, there is always some variation in the production speed of RNAs and proteins (called “gene expression noise”), which leads to variability in the concentrations of these important molecules even between genetically identical cells. Gene expression noise can threaten cellular functions, and for genes that are key to cellular survival, the noise needs to be minimized. It is not fully understood how cells can keep the noise level low for particular genes. This Mid-Career Advancement (MCA) project will enable the principal investigator (PI) to learn and apply the latest transcriptomics technologies to address this question. Experimental results will be combined with computational modeling to explain how cells can keep gene expression noise to a minimum. The project will generate new inter-institutional ties and opportunities for undergraduate and graduate students, from biology, computer science, and the data sciences, to train in cutting-edge technologies and data analysis. The educational activities will lower the barrier for biology students to use computational or mathematical tools and open more research and career development pathways for them. Gene expression noise must have a lower limit, and this limit has been widely assumed to be at a level where the variance in RNA numbers equals the mean RNA number. The PI’s group has identified genes with considerably lower variance, which cannot be explained by the standard model of expression that is used for low-noise genes. This raises two questions: (i) What is an adequate expression model for these genes? And (ii) how widespread are genes with such ultra-low noise, do they exist across organisms, and what are their commonalities? The first question will be addressed through single-molecule RNA live-cell imaging in combination with computational modeling. The second question will be addressed by surveying hundreds of mammalian genes for their level of noise using the most accurate high-throughput technology available for such measurements. Successful completion of the project will support or revise the model for low-noise gene expression that the PI’s group has developed and provide an accurate overview of the lower end of the noise spectrum in mammalian cells. This information can be applied in biotechnology to construct more reliable synthetic circuits for gene expression. 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

Computational quantum chemistry provides accurate descriptions of molecules, and it has become a standard research tool in chemistry, biochemistry, chemical engineering, materials science, and other fields. However, the equations in quantum chemistry are very complicated. This means that they are very hard to convert into computer programs, and also that those computer programs can take a very long time to run. The long computation times mean that many theoretical chemists are actively engaged in developing new theoretical models that yield faster computations, while hopefully having a minimal impact on accuracy. However, these new methods also tend to involve complicated equations that are hard to implement in computer programs. Because the programs are very complicated, they are difficult to adapt to new computer hardware that could make them run faster. This collaborative project will develop a software framework to make it much easier to implement advanced quantum chemistry methods on emerging hardware. It involves a team of experts in quantum chemistry and computer science from Georgia Tech, the University of Georgia, Virginia Tech, and the University of Memphis. This team will develop a library to efficiently handle the matrices and tensors that appear in quantum chemistry equations, and to make it easy for programmers to implement quantum chemistry methods by writing code that looks more like the equations. It will also develop a library to compute the electron repulsion integrals that are central to quantum chemistry on graphics processing units. These tools will be thoroughly tested by using them to implement several advanced theoretical methods, including coupled-cluster, relativistic, and real-time electron dynamics methods. These implementations will test the libraries and will also provide advanced simulation techniques to researchers. This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Division of Chemistry. 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

There are more than 50,000 islands in the world, accounting for 17% of the total land area and inhabited by 10% of the global population. The US accounts for 18,617 islands, where the cost of electricity such as in Alaskan and Pacific islands can be 4-8 times higher than the average in the US. The same is true for remote coastal communities, such as 200 miles of Outer Banks of North Carolina, 120 miles of Florida Keys, and many islands in the Great Lakes. For power utilities, these communities rely on imported fossil fuels or miles of umbilical cables, which are vulnerable to earthquakes, wildfires, hurricanes and storms. While the electricity supply is one of the challenges limiting socio-economic development of remote island and coastal communities, vast energy resources are available from ocean waves along the 95,471 miles of US coastline. The power density of ocean wave energy is over 10 times that of solar power and 5 times as much as wind power. Attempts to harvest this resource date back to 1799, when the first patent was issued. To date, about 250 concepts of wave energy converters (WECs) have been proposed, but none of these have achieved commercial success. There is not even a widely-accepted criterion by which to judge which WEC concept is most favorable. The objective of this project is to drive and achieve research convergence of ocean wave energy conversion for empowering remote coastal communities through transdisciplinary research across engineering, economics, environmental, and sociological dimensions. The team expects to achieve convergence for powering remote communities within 4-5 years. In the longer term, wave energy can directly benefit a large proportion of the U.S. population without long-distance transmission, since over 53% of the U.S. population is concentrated within 50 miles of the shoreline. The project will provide significant potential to improve the economic development of under-served coastal communities by identifying a practical route to renewable electricity, thereby increasing their resilience to natural disasters, and empowering the local economy. It will also substantially benefit education from K-12 to graduate students in four universities with an emphasis on professional skills development. This project will drive convergence of ocean wave energy research through community-engaged decision making, 3D techno-economic socio-environmental assessment, and transdisciplinary co-design methodology. The goal will be achieved in two phases. Phase I will develop the WEC convergence roadmap, screen and down-select 2-3 lead WEC design concepts. This will be achieved by creating 3D assessment metrics to systematically evaluate technological feasibility, economic viability, and socioenvironmental acceptability in the early foundational concept and design stage. Phase II will investigate the leading design concepts through transdisciplinary co-design and optimization, and validate the convergence through community engagement and ocean tests. Inspired by the drug discovery process, the project will use a market-pull convergence procedure based on the needs of remote coastal communities to screen various WEC concepts from the beginning. This is in contrast to the prevailing approaches in wave energy research and development. The project includes a multidisciplinary team consisting of experts in engineering, environment, sustainability, social science and an external advisory board with community end users and OEM developers to implement a transdisciplinary, community-engaged approach to this research challenge. 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 project is designed to broaden our understanding of myogenesis (muscle formation) in birds and mammals. Myogenic processes play fundamental roles in decoding genetic instructions in developing (embryonic) and adult muscles. This project will use a novel approach for analyzing gene expression pattern to advance our understanding of such developmental mechanisms. The primary focus of the project is on the chicken, a species of immense importance to U.S. and global food security. This project will support the advancement of genome science and muscle stem cell biology through graduate and undergraduate mentorship, K-12 educator training, and Extension and outreach. Curricula for high school biology classes and the 4-H Youth animal program will be created to convey applied concepts of how proper muscle stem cell function ensures muscle development. Teacher Training Workshops will be offered on genome sequencing and computational analysis of resulting data. Educators will be supported as they incorporate these concepts and methods into lesson plans for their students. These workshops will provide teachers with hands-on experiences in state-of-the-science laboratories to learn applicable skills and scalable genetics projects for use in high school settings, thus enhancing science education and fostering a deeper understanding of genetics and muscle biology among high school students. Variation in gene regulation plays a fundamental role in shaping phenotypic diversity and is crucial for adaptation, ecosystem functioning, agriculture, and medicine. However, the mechanisms by which dynamic changes in gene regulation during development influence phenotypic variation remains poorly understood. This project will utilize recent progress in genomics, high-resolution tissue imaging, metabolomics and single-cell-resolution spatial transcriptomics to reveal genetic effects undergirding gene expression and phenotype, and guide future research into the genetic basis of phenotypic variation in plants and animals. Using chickens, an integrated analysis of gene and allelic expression, along with metabolite levels, will be conducted to identify gene and metabolic regulatory networks critical to fundamental cellular processes and regulation during myogenesis. Our research will identify alleles that play a key role in reprograming or acquiring specialized cellular metabolic state, or supporting anabolic growth. We will then examine the precise roles of a subset of these alleles using primary muscle cell cultures from multiple species and a muscle cell line to validate interspecies generalizability of results. The chicken (Gallus gallus) is an outstanding system for developmental biology and studying the genetic basis of complex traits due to in ovo (egg) embryonic development and extensive diversity among domestic chickens, as well as the availability of existing genetic and high-quality genomic resources. Moreover, our prior research found that genomic imprinting has not evolved in chickens as it has in non-oviparous (non-egg laying) animal species, making poultry an ideal system to reduce confounding factors for genetic analysis. 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 IRES project provides a diverse cohort of undergraduate students with opportunities to develop the necessary skills to navigate the complexities that may be associated with interdisciplinary scholarship and international research and engagement by conducting research on ice nucleation in Austria. Through the mentorship of PI Schmale, a biologist, and the international collaborator Grothe, a chemist, students conduct field and laboratory research related to ice nucleation at the interfaces of water, soil, vegetation, and the atmosphere. The IRES project includes pre-departure educational activities and peer mentoring at Virginia Tech to help students succeed in their research pursuits abroad, followed by a 7-week field and laboratory research program in Austria. Upon completion of the research in Austria, the cohort returns to Virginia Tech for a few weeks of post-trip activities, including presentation of data at conferences and assessments. The Virginia Tech Office of Undergraduate Research provides assessment services and support. Recruitment for this IRES project is through established pipelines, including two HBCUs (Morehouse College, GA and Morgan State, MD) and Hampden-Sydney College, VA. There has been a resurgence in ice nucleation research within the last decade, largely because of the pressing need to understand the impact of aerosols on precipitation and climate. There are several important commercial applications for ice nucleation research, including the production of artificial snow, the freezing and preservation of water-containing food products, the freeze protection of infrastructure, and the potential modulation of weather. Many of these fields need new research to fill important gaps in fundamental knowledge of ice nucleation processes and their impacts. This IRES project provides undergraduate research opportunities in Austria for about 15 students over three years. Dr. Hinrich Grothe, Professor, Technical University of Vienna, Vienna, Austria serves as a non-NSF funded collaborator and hosts the students for summer undergraduate research in his laboratories. Dr. Grothe’s unique laboratory facilities include an FTIR and EPR spectroscopy lab, a chemistry lab, a fluorescence spectroscopy and microscopy lab, and a cryo-microscopy lab. Participating students frame questions and conduct research concerning ice nucleation at the interfaces of water, soil, vegetation, and the atmosphere. Content mastery of disciplinary and interdisciplinary materials related to ice nucleation is monitored with assessments administered throughout the program. This IRES project provides insights into the attitudes of students towards interdisciplinary research and explores how conceptions of collaboration and career path are affected by their participation in this unique international research experience. 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 recreational and commercial fisheries for summer flounder (Paralichthys dentatus) produce hundreds of millions of dollars in revenue to coastal economies in the mid-Atlantic and Northeast US. Recent changes in geographic distribution and dramatic fluctuations in the productivity of summer flounder, as a result of climate change and intense fishing pressure, have directly affected charter-boat captains, commercial seafood fishers, processors, dealers, and recreational fishers. Little is known about the behavioral and economic responses of policy makers and fishers to fluctuations in abundance and the total allowable catch. There has been a general movement in flounder abundance from south to north along the Atlantic coast in recent years, and this migration provides a rich opportunity to determine in detail the feedbacks between climate, fish ecology, policy regulations, and fishers who rely on summer flounder for their livelihoods, nutrition, and recreation This research will help to develop recommendations for policy solutions that meet multiple sustainability benchmarks, such as ecological sustainability, nutritional provisioning, and economic sustainability. This project will address the interactions between fish abundance, limits on allowable catch, and fish ecology from four perspectives. (1) Social scientists will use panel surveys and in-person interviews, working with community partners, to identify the fishery’s ecosystem services, its influence on human well-being, and how these have been altered as waters have warmed and the population distribution has changed. A key goal is to determine how recreational anglers have responded to recent management actions put in place in response to the changes in flounder ecology. Attitudes toward the fairness of management and level of compliance could potentially reveal aspects of angler behavior that are currently unknown to management agencies and could explain recent struggles to manage the resource effectively. (2) Economists will use analysis of contributions of the commercial flounder fishery to coastal economies to understand the consequences of changes in management. Instability in the annual flounder catch limit due to changes in flounder abundance, as well as a mismatch between historic policy and the current distribution of flounder, have changed the economic contributions of the resource. This research will better illuminate how policymakers can manage the fishery to promote resilience in both human and ecological communities. (3) Climate scientists will determine the mechanisms linking climate and oceanographic variables to population dynamics of summer flounder using existing data on relative flounder abundance and ocean conditions. This research will investigate how variable oceanic conditions determine annual variability of juvenile flounder numbers, which will reveal how climate-driven changes in oceanography affect population ecology. (4) An advisory committee with representatives from state and federal entities involved in coastal fisheries will meet each year to synthesize the social and economic data with the results from the climate analyses. These meetings will lead to predictions of the responses of this multi-faceted system under future scenarios of climate change. 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 project aims to address the impact of geomagnetic disturbances (GMDs) on submarine cables. Submarine cables are vital as they carry a significant portion of global internet traffic. Disruptions to these cables due to GMDs can lead to widespread communication outages, affecting economies, national security, and daily life. GMDs caused by space weather events like solar storms, induce geomagnetically induced currents (GICs) beneath the Earth's surface and within bodies of water. These currents can produce hazardous voltages in submarine cables, potentially leading to failures. However, the detailed behavior of these induced currents in modern submarine cables during extreme space weather is not well understood. This project seeks to characterize the induced underwater geoelectric fields (GEFs) and potential along submarine cables during various geomagnetic disturbances. The project will benefit various stakeholders, including space weather researchers, submarine cable operators, policymakers, and the broader scientific community. Moreover, this research will facilitate technology transfer and provide practical insights for disaster management and policy development. It supports the training of a postdoctoral researcher, a female early-career scientist, and a mid-career scientist, enhancing diversity and education in the field. The project aims to model interactions between ionospheric and magnetospheric currents and submarine cables during geomagnetic disturbances (GMDs). GMDs induce geomagnetically induced currents (GICs) beneath the Earth's surface and within bodies of water, posing significant risks to submarine cables, which are critical for global internet traffic. The main objective is to characterize the induced underwater geoelectric fields (GEFs) and potential along submarine cables during various geomagnetic disturbances. Specifically, the project will investigate: (1) the types of GMDs that may produce hazardous voltages, (2) how magnetospheric and ionospheric currents influence underwater GEFs, and (3) the potential impact of solar superstorms on submarine cables. The work will utilize the SCUBAS (Submarine Cable Upset By Auroral Streams) model, which predicts voltages induced in submarine cables during geomagnetic disturbances. The model leverages data from magnetotelluric (MT) studies and integrates magnetic field disturbance inputs. This research will significantly enhance our understanding of how GMDs impact submarine cables under various conditions, including extreme space weather events. The project will gain insights into the GMDs that generate significant GEFs and potential along submarine cables, contributing to better risk assessment and mitigation planning. Research fills a critical knowledge gap using a novel combination of satellite and ground-based datasets and a comprehensive computational model. The findings will aid in risk assessment, disaster management, and policy development. 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

In line with the recent CHIPS and Science Act of 2022 and the push for quantum technology, group IV-based GeSn semiconductor materials have potential in photonics due to their unique and wide range of optical characteristics achieved by bandgap engineering via control of tin (Sn) composition in the GeSn alloy. The continued development of compact and affordable laser light sources and detection based on GeSn materials is important in many areas such as communication, biomedical, and defense applications. However, one needs to synthesize device-quality, low-defect density tunable Sn compositional GeSn materials on a lattice-matched buffer. Furthermore, there is a lack of low-threshold current density GeSn-based laser light source due to the low carrier lifetime and weak carrier confinement on silicon that demand investigation of an alternative GeSn heterostructure design, which can achieve much higher conversion efficiency, thereby enhancing the integrated photonic device performance. The latticed-matched combination of GeSn and InAl(Ga)As heterostructures for laser and photodetector offers a new path for highly efficient tunable light emission and detection. The central thrust of the proposed research is to investigate the design of tunable Sn compositional GeSn-based laser and mid-wavelength infra-red (MWIR) photodetector architectures that combine GeSn quantum-well (QW) or absorber layer lattice-matched to underlying InAl(Ga)As buffer. Our objective is to develop tunable wavelength laser light sources that can exhibit lower threshold current density and higher quantum efficiency than existing group IV-based light sources as well as MWIR detection that will benefit a wide range of applications. To demonstrate the feasibility of the proposed approach, several key technical and scientific challenges must be addressed, including (i) low-defect density tunable Sn compositional GeSn layer on lattice-matched InAl(Ga)As buffer with enhanced carrier lifetime; (ii) increased band offsets between GeSn and large bandgap InAl(Ga)As barrier layer for superior carrier confinement in a GeSn QW; (iii) design and simulation of the proposed wavelength tunability of GeSn/InAl(Ga)As-based QW laser and MWIR GeSn-based photodetectors; (iv) materials synthesis and analysis of lattice-matched InAl(Ga)As/GeSn/InAl(Ga)As QW laser structure on GaAs for modified bandgap of GeSn; and (v) fabrication and demonstration of GeSn QW laser and photodetector. To address (i), (ii), (iv), and (v), the proposed research will utilize the state-of-the-art in-house epitaxial growth (interconnected III-V and group IV molecular beam epitaxy chambers), comprehensive materials characterization and simulation (e.g., high-resolution x-ray diffraction, transmission electron microscopy, photoconductive decay, photoluminescence spectroscopy, atom probe tomography, x-ray photoelectron spectroscopy, and electronic band structure simulation by QuantumATK), and in-house fabrication facilities. To address (iii), a combination of numerical simulations (Synopsys TCAD) and density functional theory will be leveraged to develop experimentally-calibrated InAl(Ga)As/GeSn/InAl(Ga)As QW device models necessary for light emission in MWIR range and photodetection. By investigating these topics, this research will elucidate numerous as-of-yet unexplored avenues of fundamental research, including (a) the synthesis of high Sn compositional GeSn alloy on lattice-matched InAl(Ga)As buffer; (b) the role of GeSn layer thickness to optical gain and emission wavelength; (c) the reduction of threshold current density arising from recombination losses; (d) carrier lifetime and interatomic diffusion in a GeSn alloy on InAl(Ga)As buffer. Through a comprehensive examination and understanding of the above challenges, this research will establish a pathway to achieve high-performance group IV lasers and detectors that will benefit society and industry. Furthermore, this project will train and mentor undergraduate and graduate students in the field of photonics. These students will experience a research environment in PI’s laboratories. Outcomes of the proposed research results will be disseminated to the public and be incorporated into the course curriculum. In addition, the project will provide hands-on experience to undergraduates through Virginia Tech ECE Department major design experience. 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 award supports research that seeks to answer a fundamental question about matter. What is the nature of new physics not described by the Standard Model - our current framework of how particles and forces interact? This question will be addressed by a precise measurement of the weak charge of the electron in a large collaborative experiment at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) in Newport News, Virginia. The electron’s weak charge is the weak force analog of the common electric charge of the electron, and it is precisely predicted in the Standard Model. Any deviation of the measurement from this prediction could be evidence of yet undiscovered massive particles that represent new physics beyond our current understanding. The program will support graduate and undergraduate students, who will form an essential part of this project. The supported activities cover a range of hardware and software tasks that will provide excellent training for these individuals. This training in fundamental research provides a strong basis for careers both in basic research and in more applied research and industry. The funds provided by this award will support activities on the MOLLER experiment, a parity-violating electron scattering experiment at Jefferson Lab. The MOLLER experiment is an approved experiment making use of the upgraded 12 GeV accelerator at the lab. It will measure the electron’s neutral weak charge by making a precise measurement of the parity-violating asymmetry in the elastic scattering of polarized electrons from unpolarized electrons at very low momentum transfer. This will result in a determination of the weak mixing angle at low energy with a precision comparable to the best determinations from high energy colliders. The result would have excellent sensitivity to many beyond the Standard Model scenarios. During the period of this project, the research team will make important contributions to this experiment: construction and installation of the scattered beam monitor and scanner systems, coordination of the MOLLER beam charge monitor upgrades, installation of a halo monitor system, and data analysis code development. 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 RAPID project aims to address how electric utilities, particularly those serving isolated and remote areas, can securely deactivate the vulnerable segments of their power grids during wildfire events while ensuring that essential home medical assistive devices maintain adequate power during the shutdown periods. This problem is more significant for Hawaii, given its island geography, recent destructive wildfires, and aging population. The intellectual merit of this project lies in its innovative approach to advancing the knowledge of how vulnerable portions of an electric utility microgrid can be deenergized while minimizing the life-threatening risks of outages. The broader impacts of this project are that even modest improvements in wildfire resilience of the electricity infrastructure can result in substantial financial savings and mitigation of adverse social, psychological, and physical consequences of wildfires. Additionally, this project will create materials for educating electric utility emergency operators while disseminating the results via media outlets. The project has three main tasks. Tasks 1 compiles a comprehensive list of electric-powered home medical equipment through time-sensitive data collection, surveys, and interviews. Task 2 identifies flexible grid resources, dynamic microgrid reconfiguration strategies, and hidden capacities for safe deenergization of vulnerable portions of the grid before, during, and after wildfires. Task 3 proposes a novel time-triggered de-energization framework that accounts for both primary and secondary feeders within a microgrid to enable an in-depth exploration of the technical constraints involved in partial deenergization. Studies will be done to investigate scenarios involving vulnerable load points, varying levels of accessible backup and mobile resources, and a diverse spectrum of available flexibilities. The main deliverables include generating datasets, documentation of the proposed strategy, and validation of results. 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 project advances predictive digital twins of jet engines. The digital twin is a virtual object representing the jet engine and will be used to inform decisions such as design, optimization, operation and maintenance. This is crucial to enhancing aircraft safety whereby the digital twin enables a proactive approach to identifying and resolving potential issues, and to scheduling preventive maintenance. The digital twin allows a better understanding of the physical behaviors that an engine would exhibit under many operational scenarios, some too dangerous for physical experimentation. The research directly engages undergraduate students through teaching laboratories and a more in-depth engagement program targeted toward students from underrepresented and under-served groups in engineering. The project also involves training of doctoral students in Computer Science, Mathematics, and Engineering. The overarching goal of this project is to expand the mathematical foundations of digital twins with application to jet engines, and to increase their predictive simulation capabilities by fusing information from advanced modeling and state-of-the-art measurements. It develops high-fidelity multiphysics models for jet engine combustion and flow, as well as scalable reduced-order models, both with quantified uncertainties. Particle-surface interaction models are constructed to quantify erosion and deposition effects on engine performance. An array of novel hierarchical data assimilation algorithms are developed using variational approaches, ensemble Kalman filters, and transport map particle filters, all in the context of a hierarchy of models. An innovative experimental setting at the Virginia Tech Advanced Propulsion and Power Laboratory with a JetCatP100-RX engine allows testing with the physical twin. 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.