North Carolina State University

NSF Awards · FY 2024 · 2024-10

The "Undergraduate Research Opportunities in Unmanned Systems Foundations and Applications" Research Experiences for Undergraduates (REU) site at the University of Nebraska offers an intensive and comprehensive research experience to ten undergraduate students per year in the context of unmanned systems, a rapidly growing field of scientific and technological research. A team of faculty experts from the Nebraska Intelligent MoBile Unmanned Systems Lab (NIMBUS Lab) provides individual mentoring for ten weeks to instill the foundations of research methodology and to conduct hands-on research activities on a variety of unmanned systems applications. The participants engage in professional development activities to better prepare them for Science, Technology, Engineering or Mathematics (STEM) careers. Students from computer science, computer engineering, mechanical engineering, electrical engineering, and other related majors are considered. Particular attention is given to students from underrepresented groups and from institutions in the Midwest that lack research opportunities to support broader educational goals and diversity in the STEM workforce. Each REU participant's research project focuses on unmanned systems with topics including close interactions of aerial robots with the environment; proficiency development in operating robotic systems; vision-based control for collaborative robotic systems; multi-agent design, control, and applications; and resilient, synergistic communication systems. Projects build on ongoing faculty research but are crafted for participants to gain experience in all aspects of research, from conducting a literature review and prototyping, to understanding the broad potential impact of the technology being investigated. Participant development will be quantified by closely assessing their growth through the REU experience and maintaining mentorship relationships after the summer. This site is jointly funded by the Department of Defense in partnership with the NSF Directorate of Computer and Information Science and Engineering REU program. 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 planning project will engage the Computer and Information Science and Engineering (CISE) community on millimeter-wave (mmWave) and advanced wireless research to gather feedback and gauge support for a new research infrastructure that introduces the principles of open, disaggregated, and “softwarized” radio access networks to mmWave systems. This planning project will analyze research priorities, platforms, and interfaces to inform the development of an experimental research infrastructure combining flexible radio capabilities and control loops based on Artificial Intelligence (AI) within mmWave systems. The intent is to enable adaptive wireless experimentation over high-frequency systems, offering new opportunities for creating, training, evaluating, and improving mmWave systems on realistic, over-the-air scenarios. The goal of this planning effort is to (1) understand the needs of the CISE community; (2) scope enabling technologies and architectural building blocks; and (3) lay out the design for an adaptive mmWave system that would spur experimental research and development exploring high-frequency bands for 6G and beyond. This planning project will provide an initial assessment of the need and potential for an adaptive mmWave research infrastructure, adopting the principles of open and softwarized radio access networks to mmWave systems. The planning activities involve (1) mapping and interviewing relevant stakeholders, including members and officers of the O-RAN Alliance, the Next G Alliance, and PAWR (Platform for Advanced Wireless Research) facilities; (2) conducting community surveys across academic, industry, and government participants to understand their research priorities, needs, and pain points; and (3) visiting existing wireless testbeds to gather insights into their capabilities and limitations, as well as to identify enabling technologies and architectural building blocks. This project will co-design the research vision and infrastructure architecture with the CISE research community and contribute back by disseminating its findings, potential use cases, and designs to help support and motivate additional research and important standardization and regulatory decisions related to high-frequency bands for 6G and beyond. 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

Methods to stabilize enzymes to improve their performance in industrial processes have been pursued for decades. A promising approach combines enzymes with synthetic polymers. Attaching enzymes to synthetic materials has been shown to enhance their recyclability. This approach has also been shown to decrease enzyme denaturation in extreme environments. However, little is understood about why certain materials stabilize some enzymes but not others. The overall goal is to understand and develop design rules on how to stabilize enzymes via immobilization to complex synthetic materials. This project will also provide multi-disciplinary training for graduate, undergraduate, and high school students. Project results will feed into an annual data science capstone project. Protein stabilization can be regulated by tuning the composition of random copolymer brushes to which the protein is attached. A detailed understanding of the molecular basis of this approach is critical. This understanding will be achieved by combining functional stability measurements, single-molecule methods to quantify conformational dynamics (e.g., unfolding and re-folding rates), and atomistic molecular dynamics simulations. Using this approach, the hypothesis that the chemical properties of the brush layer and enzyme surface should be well-correlated. To systematically test this hypothesis, several closely related, but structurally diverse lipases will be used. Single-molecule Förster resonance energy transfer and simulations will be used to distinguish between possible mechanisms of stabilization. Mechanisms to be evaluated via tuning the enzyme-brush interface, will include enhanced re-folding (i.e., a chaperone-like effect) and reduced unfolding. Additionally, the salient chemical features of the brush layer that contribute to the stabilization of enzymes will be identified. This work will leverage a novel algorithm to model and identify clusters of hydrophobic atoms on protein surfaces using unsupervised machine learning. The results of this work are expected to lead to transformational advances in industrial biocatalysis. The impact may extend to other fields, including biosensing, bioremediation, and smart materials. 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 collaborative project brings together investigators from Virginia Tech, George Mason University, and the University of Surrey to develop a hardware-in-the-loop (HIL) platform that facilitates the research, development, and experimentation with adaptive communications for multi-constellation space network coexistence. The main contribution is SpaceNet, the first-of-its-kind, fully open-source HIL space network testbed that is able to emulate a fast-changing network topology with changing connectivity and latencies, challenging routing, transport protocols, and re-architecting applications. The testbed will uniquely support multiple networks of distributed heterogeneous platforms in multiple domains, addressing challenges in adaptive communications for multi-constellation coexistence, emulating what has been called the “internet of space things”. Direct connectivity allows for the exploration of how satellites and constellations might interconnect without revealing proprietary information of each constellation, creating the equivalent of interdomain routing for space networks. Additional capabilities examine behavior in the face of disruptions such as large solar storms. Achieving these new capabilities requires merging lab-based spacecraft hardware with a large-scale state-of-the-art wireless radio testbed to create more flexible and higher fidelity rendering of the satellite links, resulting in a networked HIL development and a testing environment that accurately models the network and dynamics; routing and re-routing data link tables on the fly, scaled to multiply-redundant algorithms and architectures; satellite-like lab hardware for testbed nodes; and virtual nodes and remote access for government, industry, and academic researchers. The expected outcome is a remotely-accessible, multi-domain network and cybersecurity research infrastructure, and a validated first-of-its-kind adaptive and assured space network communications emulator. In addition, students will be involved in class projects covering technical concepts on space communications, TN/NTN co-existence, and hands-on experiences with SpaceNet. Project outcomes will be widely disseminated via workshops, tutorials, software repositories, peer-reviewed conferences, and scholarly archival journals. The broader wireless communications communities will be engaged in the design, development, use, and maintenance of the proposed CISE infrastructure. The project repository will be linked from the proposal and milestones page at https://space-net.org at least through the duration of the project, and will be used for sharing research updates, workshop livestreams, dataset, code, and preprints. 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

Like taking an opinion poll, down sampling is a key technique for grappling with the ever-growing amounts of data used in large-scale machine learning (ML) tasks. In particular, the objective function optimized when training ML models is defined over a full set of data, but evaluating this objective function repeatedly during training is impractical, whereas evaluating the objective function on a sample of the data scales much better with minimal loss in accuracy. Instead of sampling uniformly, the sampling process can be biased to prefer parts of the data set that have a greater influence on the objective function, resulting in faster convergence and smaller errors of in the learning process. The classical approach to sampling, known as Markov chain Monte Carlo (MCMC), assumes that the current sample has no useful dependence on the samples that came before, known as the Markov assumption. It remains to be seen whether a non-Markovian sample process that allows for an explicit dependence on past samples, can be turned into a sampling process that translates to a better ML training procedure, either practically or theoretically. The overarching theme in this project is to transcend the current limitations in sampling, optimization, and machine learning algorithms that have predominantly been built upon Markovian approach or MCMC, by exploiting the full potential of going beyond traditional Markov chains for the analysis and design of distributed algorithms in the most efficient way. Specifically, this project aims to explore the following three inter-related thrusts. The first is to explore all possible ways to maximally enhance the sampling efficiency of multiple, interacting nonlinear Markov chains in the form of self-repellent random walks (SRRWs) by designing adaptive degrees of spatio-temporal repellency among multiple walkers as well as with their `collective' history, while providing all the theoretical performance guarantees. Second, this project will assess the performance implication of distributed algorithms in ML/optimization and decentralized learning in the form of stochastic approximation and its variants, when driven by a set of adaptive and interacting nonlinear Markov chains such as SRRWs instead of traditional MCMC inputs, and obtain usable performance bounds both in finite-time/sample and asymptotic regime to strike a balance between faster convergence and maximal efficiency. Third, this project seeks to develop an algorithmic framework in which one can, for a given Markovian environment, always speed up the stochastic approximation algorithm by augmenting it into multi-timescale versions with low computational complexity, as well as co-design them with carefully constructed nonlinear Markovian sampling strategies for a tunable environment in decentralized learning. Broadly speaking, this project will have potential impact on a vast range of multi-disciplinary applications where the standard MCMC methods and Markovian-driven stochastic and iterative algorithms have been dominant and taken for granted, including sampling from high-dimensional state space with graphical constraints, Markovian random walks on general graphs and their applications to various inference tasks in a distributed manner, learning algorithms and stochastic approximation in a Markovian environment, stochastic optimization with Markovian noise, and beyond. 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 last few decades have brought rapid innovation and cheaper deoxyribonucleic acid (DNA) based technologies. At the same time, the massive amount of information created by society has created a new urgency to discover and develop better data storage technologies that can more densely and cheaply house data for long periods of time. Together, these trends have motivated broad interest in harnessing the density and robustness of DNA to store vast troves of digital information. To date, only small-scale prototypes of DNA-based data storage have been demonstrated by academia and industry. These systems were studied at a scale far below that which is needed to deploy useful storage technologies. A major challenge in scaling up to practical applications is that many distinct molecules are crowded in a very small space without any spatial order. These molecules interact in highly complex ways that currently exceed the limits of scientists or computer simulations to directly study in detail. Prior work attempted to limit such interactions in the design of the DNA molecules so that the systems would be easier to understand and control.  However, as a result, scalability is also limited, and the richness of possible molecular interactions is curtailed. This project takes a different view, hoping to leverage even weak interactions between molecules in useful ways, inspired by the complex chemistries of life. New computational approaches, chemistries, and wet lab experiments will be developed to understand and discover new and better ways of modeling and designing these systems. The findings will be used to create new or improved functionalities like accessing data from a repository, searching for specific data, and securing data. A team of researchers at NC State University in computer engineering and chemical and biomolecular engineering will bring their joint expertise to analyze, understand, and forge solutions that will enable DNA storage at larger scales. Society faces a major challenge of securely, affordably, and sustainably preserving digital data. Current technologies are not scaling in size or cost quickly enough to keep up with the massive amounts of data being created. DNA has the potential to solve this problem due to its enormous density and longevity. The research findings from this project can help realize DNA as a storage technology by addressing critical barriers of scaling DNA and by unlocking innovative ways of organizing, accessing, searching, and securing data that’s stored in DNA. This project will also support the training of graduate students, produce online educational materials to help advance knowledge in this field, and create opportunities for under-served K-12 students to learn more about data storage through summer camps at NC State University. 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 NSF CAREER project aims to investigate power system dynamics with large-scale integration of inverter-based resources (IBRs), which challenges the existing frameworks of stability simulation and analysis. The project will bring transformative change to power system dynamics studies and improve the accuracy and efficiency for capturing dynamic responses in both fast the slow time-scales. This will be achieved by creating a unified framework that blends fast transients with slow dynamics and selecting dynamics through transformation, simplification, and numerical methods. The intellectual merits of the project include establishing a unified symbolic framework for device- and system-level modeling, developing advanced analytical methods for stability analysis, and creating efficient simulation algorithms, all aimed at improving grid simulations under complex, multi-timescale dynamics. The broader impacts of the project include enhancing open-source infrastructures for power engineering research and education, cultivating public interest and knowledge of renewable energy through innovative outreach programs, and engaging underrepresented students with hands-on experiences in renewable energy technologies. The large-scale integration of converters and IBRs has significantly impacted power system dynamics. Traditionally, the notion of time-scale separation facilitated a classification between component-level fast electromagnetic transients and system-level slow-varying electromechanical stability. This separation, however, is being challenged by IBRs, which interact with both fast network transients and slow electromechanical dynamics. This project aims to understand how network transients, switched converters, and electromechanical dynamics can be uniformly modeled, rigorously analyzed, and efficiently simulated. Specifically, the project will 1) establish a symbolic framework for formulating component dynamics by switched differential algebraic equations (DAE), which will enable the transformation and simplification of models in a principled manner; 2) establish analytical methods to characterize the oscillatory properties of small-signal models, including assessing the impact of uncertainty on eigenvalues and timescale separation; and 3) develop efficient simulation methods for switched DAE problems in both the time domain and dynamic phasor domain, creating algorithms that leverage the properties of the mathematical models to speed up computations while maintaining accuracy. 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

Physics constitutes a fundamental science discipline that underpins various STEM fields and represents the cornerstone for future workforce proficiency in technology. Yet, physics is also known for its reputation as one of the most academically demanding subjects. In particular, comprehending foundational mechanics concepts like force and moment poses a significant challenge due to their abstract nature, making them difficult to grasp through traditional laboratory experiences. Moreover, high school students with a low socioeconomic status tend to avoid enrolling in physics classes, partly due to their low physics self-efficacy and sense of not belonging in the physics classroom. This project will leverage low-cost tablet computers and force sensors to permit high school students to engage with interactive augmented reality (AR) learning experiences that integrate both visual and haptic feedback, supported by complementary curriculum modules covering Newtonian mechanics and electrodynamics. By creating an affordable AR platform that integrates force sensing and force visualization in real time, and connects physical manipulations to the free body diagrams typically used to represent forces, formerly abstract concepts are made concrete. This project thus aims to make physics education (and the careers dependent on it) accessible to a broader range of students. This project will construct a low-cost force sensing platform capable of measuring both 3D contact and non-contact forces and moments, integrated with a tablet computer that will use AR to visualize 3D forces and moments. The companion curriculum modules will target Newton’s Third Law and electromagnets, encouraging learners to visually and tactilely explore their properties. The work will use a design-based research approach to ask: (1) In what ways do students employ interactive visualizations of force vectors, and what new learning opportunities and practice does this experience offer both students and educators?; and (2) To what extent do visuo-haptic experiences impact students’ understanding of science concepts relating to force and moment vectors? The first question will be addressed via a controlled lab-based study with 45 high school students and 8 physics teachers, where a rich array of data will be collected, including think-alouds, participants' generated work, field notes from researchers, classroom videos, screencast recordings, interviews with both students and teachers, and a survey designed to collect de-identified demographic and socioeconomic information, prior academic achievements, science knowledge, and experience with AR technology. The second question will be addressed via a pilot study in a high school, which will administer Force Concept Inventory pre-and post-tests to contrast students who use the developed device and curriculum against students using a pen-and-paper lesson. The results from both studies will shed light on how learners make sense of physics concepts with a multisensory visuo-haptic learning experience, and the extent to which these experiences might improve physics learning. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and 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

Black disabled students encounter systemic challenges in K-12 education such as being overrepresented in special education categories of behavioral and intellectual disabilities while facing harsher disciplinary consequences compared to other students. These challenges impact their opportunities for meaningful STEM learning. A key avenue to counter these disparities is through high school mathematics teacher coaching encompassing knowledge of the interactional nature of racism and ableism in teaching and decision making. Therefore, this project aims to develop and test a theoretical coaching framework that addresses challenges while advancing conceptual mathematics learning and high school mathematics instructional practices. Using qualitative participatory methodology, this project will involve establishing and sustaining an authentic partnership with a cohort of Black disabled high school students. Their voices, knowledge, and experiences will be central in informing the development of this project’s coaching theoretical framework. The research team will support students’ learning, developing, and enacting ways to counter racism and ableism, advance conceptually oriented mathematics instructional practices, and impact instruction to improve students’ experiences and learning opportunities. Students will have opportunities to convene to share their experiences, and mathematics teachers will participate in professional development opportunities to support working with students as well as piloting and developing the coaching model. This project will contribute to both theory and practice in mathematics education as well as produce positive impact to the lives of Black disabled students. 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

Plant roots contain a diverse community of microbes that help the plant grow, uptake nutrients, and resist stress. Therefore, there has been a lot of interest in using these microbes to improve the growth and productivity of agricultural plants. Unfortunately, in many cases these microbial products fail to provide long-term benefits to the crop. This is because scientists still lack a basic understanding of what makes a microbe able to survive on the plant root. To overcome this challenge, this project will implement novel genetic engineering techniques to discover genes that, when added to a microbe that cannot survive on plant roots, enable that microbe to survive longer on the roots. This research will be performed on several different types of plants under several different nutrient conditions and with several different types of plant-associated microbes. Together, this project will allow the identification of microbes with the potential for long-term benefits to crop growth and productivity. In parallel, this project will implement a two weeklong summer program to introduce high school students to the importance of microbes in agriculture and organize reciprocal research exchange visits between graduate students at North Carolina State University, University of Kansas, and the Hebrew University of Jerusalem. Our understanding of the genes and mechanisms that modulate bacterial abundance in/on roots remains incomplete, because prior work has either focused on correlative relationships between genes in root-associated versus non-root-associated bacteria, or genes that are “necessary” for colonization in an already good colonizer. Much less studied are genes that are “sufficient” to improve colonization in a poor colonizer. To identify and study such genes, the project will 1) generate functional metagenomic libraries using soil and rhizosphere DNA, and apply these libraries to plants to explore the effects of host genotype on genetic functions enhancing microbial load on and in the root, 2) investigate the impact of microbial community composition on the functions enhancing microbial load on and in the plant root, 3) investigate the impact of abiotic stressors on the functions that enhance microbial load on and in the root, and 4) test whether load-enhancing functions are enriched in root-associated metagenomes and that removal of these genes from soil microbes reduces their load on and in roots. Taken together, this work will identify colonization-enhancing factors for the plant root, thus opening the door to other valuable phenotypes including pathogen resistance and nutrient provision. It will also reveal to what extent the functions encoded within these genes are specific to certain plant varieties, species, microbiome compositions, and abiotic conditions. 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 United States faces the critical need to prepare students and the future workforce for advances in Artificial Intelligence (AI). This project will develop curriculum that will engage middle-school students in learning science and basic AI concepts and in developing related career interests. The curriculum will first introduce students to the most fundamental approaches in AI-based problem solving. Next, students will learn how to use this knowledge to solve real world problems within an innovative learning environment, adapted to facilitate integrated science and AI problem solving. To contribute to supporting students who are underrepresented and underserved in STEM, the project is designed to foster an inclusive learning environment and provide access to resources and opportunities that promote equitable participation. Project research will investigate the impact of the curriculum in improving students' learning and in cultivating STEM interest and career aspirations. Over 900 middle-grade students from diverse backgrounds will participate in the research. The project will conduct much-needed research on AI education that facilitates student learning with block-based, visual programming that uses graphical blocks to represent coding concepts instead requiring the writing of complex code. (Users can “drag and drop” these blocks to form sequences that create a program.) Four curriculum units that integrating different science disciplines will be developed. Student learning experiences will be supported by the user-friendly design of a block-based program environment. Teacher will participate in professional learning opportunities introducing AI and block-based programming. A design-based research approach will be employed, proceeding through an iterative process starting with co-design with teachers, followed by field testing, and culminating in a quasi-experimental study in the final year of the project. The research will focus on three areas: (1) How does the curriculum fosters students’ learning of science practices and AI concepts? (2) How do these student learning experiences influence their abilities to create AI solutions to science-focused problems? and (3) How does the curriculum stimulate and sustain student interest in science, AI, and related careers? Qualitative and quantitative data will be collected. The quantitative data will include pre and post-tests to measure learning and questionnaires on student interest in science and AI. Qualitative data will include video and audio recordings of classroom implementations, peer interactions, student-teacher interactions, and student use of project technologies. The project will disseminate curriculum materials, teacher guides, and research findings to educational researchers and educators through teacher workshops, conference presentations, journal publications, outreach activities, and popular social media outlets. The project is supported by the Discovery Research preK-12 program (DRK-12), which seeks to significantly enhance the learning and teaching of science, technology, engineering, and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models, and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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

Recent advancements in data science and machine learning have enabled the resolution of complex problems in science and engineering, for instance, to explain why and how photoplasticity occurs in semiconductors. Photoplasticity is a phenomenon where light exposure can cause materials to harden or soften. Although known since 1957, the mechanisms behind it remain unclear due to the lack of high-fidelity tools that can address the full complexity of a light-matter interaction. To meet this need, this Computational and Data-Enabled Science and Engineering (CDS&E) award supports the development of a multiscale simulation tool that fuses quantum, atomistic, and mesoscale models together through machine learning. The goal is to establish simulation tool and produce database that can advance our understanding on how semiconductors deform under light exposure. This research will promote progress in science and benefit industry technologies in flexible electronics, deformable solar panels, and so on. Additionally, this project will equip the next-generation workforce with a broad range of knowledge and skills in data science, machine learning, and computational mechanics. This project aims to establish an electronic-to-mesoscale simulation tool for predicting the deformation behavior of semiconductors under light. By choosing zinc sulfide as an example, at the electronic level, a large-scale dataset will be first created from systematic constrained density functional theory calculations. This dataset will then be consolidated into light illumination-affected interatomic force fields through machine learning. To scale up in length, such machine learning-based force fields will be informed into nanoscale molecular dynamics (MD) and mesoscale coarse-grained (CG) models to simulate the interactions between light and nanometer-/micrometer-long dislocations, which are line defects and main carriers of plastic deformation in semiconductors. To get one step closer to experimental conditions, the dislocation mobility laws and plastic flow rules will be synthesized from the MD and CG simulation data, which can be used to interpret the photoplasticity observed in laboratory tests. The resulting multiscale computational framework and knowledge database are general. They can be applied not only to photo-plasticity, but also to electro- and chemo-plasticity various materials and devices such as optical sensors, solar cells, photocatalysts, where the interaction between electrons and dislocations become important but is difficult to probe in experiments. 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

Increase in atmospheric carbon dioxide (CO2) is a major cause of global climate change. One of the most effective nature-based solutions to this challenge lies right under our feet - the soil. Globally, soil contains more carbon than in the Earth's atmosphere and vegetation combined. National and international initiatives are in place to increase soil organic carbon (SOC) content and storage capacity to combat climate change. The multifaceted benefits of SOC storage can also ensure food and nutritional security for the Earth's human population and help meet many of the United Nations Sustainable Development goals. However, it is not clear how long soil can provide these ecosystem services to our global community. This is partly because SOC data available from various sources and predictions based on computer models don’t agree with each other. This project aims to provide a robust estimate of SOC for the conterminous United States (CONUS), which can help identify potential reasons for inconsistency across different models and ultimately facilitate policy-makers in making informed decisions about climate change. It will also offer research training opportunities for students as well as workshops and training courses for teachers. For the U.S., there is a unique opportunity to use spatial clustering approaches to reduce uncertainties in SOC dynamics and constrain models at the continental scale by upscaling site-based measurements across the National Ecological Observatory Network (NEON). Emergent ecosystem properties will be evaluated by using multivariate quantitative methods to extrapolate or interpolate point-scale SOC measurements from a spatial constellation of NEON terrestrial sites to CONUS. Data collected across NEON terrestrial sites will be coupled with an array of multivariate geographic clustering algorithms (k-means clustering, ensemble clustering) and machine-learning (convolutional neural network, artificial neural network) approaches. These quantitative analyses will also enable uncertainty quantification of spatial representativeness of SOC and help identify potential future relocatable (or mobile) sites for additional ground-truth measurements of variables related to terrestrial C cycle processes. Existing NEON biogeochemistry, microbial, hydrology, sensor, and remote sensing data products will be leveraged to produce quantitative SOC regional maps for CONUS using similar combinations of climatic, ecological, environmental, geochemical, and microbial variables. The algorithms developed with NEON data will be validated with other point-scale data like SoDaH (SOils DAta Harmonization database) and ISNC (International Soil Carbon Network). The spatial mismatch of derived representativeness-based SOC regional maps for CONUS will be evaluated with existing gridded databases: SoilGrids, Harmonized World Soil Database (HWSD), Northern Circumpolar Soil Carbon Database (NCSCD), and gridded U.S. Soil Survey Geographic Database (gSSURGO).EON-based SOC regional maps for CONUS will also be integrated with downscaled historical SOC predictions from participating models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The robust (and scalable) estimate of SOC for CONUS will enable the diagnosis of terrestrial C cycle processes using historical CMIP6 model runs. Broader impacts will involve training opportunities at the undergraduate and graduate levels, and workhops and training courses to teach data analysis workflow methods. 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 will broaden participation in engineering by developing learning resources through which Black families have opportunities to engage in engineering practices and to see themselves as part of the engineering community. The research team will co-develop informal learning resources with Black families in which children, ages six to ten, have opportunities to engage in biological, civil, computer, electrical, environmental, and mechanical engineering activities at home. Caregivers will support their children through engineering practices such as empathizing, defining, ideating, prototyping, and testing, while also educating them about Black engineers and scientists who made significant advancements within each field. Research will explore whether and how the identity-affirming informal learning resources fostered the children’s engineering identities and interest. The resulting deliverables include video workshops for caregivers, to support them in using the resources, as well as a suite of easy-to-use engineering activities that will be disseminated via national homeschool networks, through public media, through high-traffic repositories with engineering lesson plans, and through professional networks of science and engineering educators. Research will explore how identity-affirming engineering educational resources impact children’s engineering identities and interests. To investigate whether and how these resources contribute to shifts in children’s engineering identities and interests, the research team will conduct a mixed-method study in which they generate and analyze the following data sources: pre- and post-engagement surveys with the caregivers; video-recordings of caregiver-child interactions as they engage with the informal learning resources; interviews with children and caregivers; caregiver reflective journals; and artifacts produced by the families, such as children’s sketches. The results from these analyses will provide insights into how informal educators can design at-home learning resources that build children’s interests in engineering pathways, as well as how families can use identity-affirming interactions in engineering to spark their children’s interest in this field. Findings will be disseminated widely via professional conferences, networks, and journals in educational research. Ultimately, this project is likely to broaden participation in engineering among Black people who remain underrepresented in engineering pathways and careers. This Integrating Research and Practice project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of STEM learning in informal environments. 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 East Coast Operator Algebra Symposium (ECOAS) is an annual research conference centered around the theory of operator algebras and their applications. The first meeting was at Vanderbilt University in the Fall of 2003, and since then meetings have occurred annually. This award will partially support participants for this year's event, the 20th meeting of ECOAS, held at North Carolina State University in Raleigh, North Carolina, November 9-10, 2024. The conference will provide a venue for early career researchers to learn about developments at the forefront of their field, to share their work with the broader community, and to network with other early career researchers as well as more senior members of the community. This event focuses on C*-algebras, von Neumann algebras and a wide variety of applications, including to quantum physics, representation theory, and dynamical systems. Thirty to sixty participants are expected. The plenary speakers will review recent advances, enabling participants to keep abreast of recent developments in a vast and rapidly expanding subject. More information is available at the conference website https://www.coreyjonesmath.com/ecoas-2024. 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

Computer science education is increasingly critical for preparing well-trained professionals for the national economy and building a competitive workforce of the future. The emergence of generative AI provides an opportunity to improve computer science education by adapting the learning process to the needs and knowledge of individual learners. University of Pittsburgh, Carnegie-Mellon University, University of Massachusetts, and North Carolina State University will develop and evaluate a comprehensive personalized programming practice environment (C-3PE) that utilizes artificial intelligence (AI ) to enhance learning experiences. This project capitalizes on the power of generative AI and progress in learning science research to provide personalized learning experiences for computer science students. C-3PE recommends the most suitable learning activities for each student according to their current knowledge level and offers personalized feedback to support their progress. By conducting long-term classroom studies, the project team will assess the impact of AI-based personalization approaches and identify the most effective types of learning activities and feedback messages for students with different competency levels. Leveraging advances in AI-driven learning technologies and theoretical frameworks in learning sciences, C-3PE will deliver engaging computer science learning experiences. It will provide personalized practice support and detailed feedback for individual learners based on their practice history and current knowledge state. C-3PE will dynamically model the state of learner knowledge using context-aware deep-learning knowledge tracing models. Furthermore, the project team will develop a nested personalization approach with an outer loop and an inner loop. For the outer loop, the project will develop new, large language model (LLM)-powered adaptive testing algorithms that select the most informative next practice question/worked example for each student. For the inner loop, they will use preference optimization to align LLM-driven feedback generation with student learning outcomes. A sequence of experiments will lead to a better understanding of the kinds of practice opportunities (i.e., worked examples vs problems) and types of feedback messages that are most effective to each student. Utilizing an iterative design process to integrate insights from studies into the learning environment, the project will evaluate C-3PE in various introductory programming classrooms across diverse institutions. The project will enhance education through personalized recommendations and feedback, disseminating findings and tools through academic conferences and platforms, and sharing C-3PE via a GitHub repository for computer science instructors. This project is funded by the Research on Innovative Technologies for Enhanced Learning (RITEL) program that supports early-stage exploratory research in emerging technologies for teaching and 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

Economic development, increasing population, lifestyle changes, climate change, and underlying policies exert stress on both natural resources and aging infrastructure. Given the cost and timescales required to invest in new infrastructure (e.g., reservoirs, power systems), managing existing infrastructure better is critical. Further, in developing countries, limited institutional capacity is in place to mitigate environmental hazards which require significant coordination and preplanning among agencies. Hence, to improve disaster preparedness and infrastructure management against hydroclimatic extremes, this AccelNet project brings together national and international subseasonal-to-seasonal (S2S) forecasting agencies and sectoral agencies associated with water, power, and public health to accelerate the uptake of S2S forecasts forecast applications and services (FAS). Towards this, we will build a network of networks (NoN), S2S AccelNet, with 10 core partners and 13 collaborating institutions across the US, Africa, Brazil, and India to reduce societal vulnerability to climate, environmental hazards, and health risks. The rationale is to reduce critical gaps in FAS, incorporate FAS in regional climate outlook forums (RCOFs) and develop an interdisciplinary curriculum and workforce focusing on climate-informed adaptive management for various services. The key objectives of the S2S-AccelNet project are to: 1) identify grand FAS research opportunities and challenges in FAS data availability and models, and policy issues; 2) recommend potential pilot basins/regions for FAS modeling, research and synthesis; 3) develop an open-source platform and knowledge base — S2S-AccelNet Hub — for better researcher-stakeholder engagement; and 4) execute interdisciplinary outreach and early-career exchange programs with 10 core and other collaborating networks and educational institutions. The intellectual merit of this project is towards building S2S-AccelNet Hub with pilot applications, which will promote convergent research on S2S FAS, together with improved strategies and knowledge for rapid sectoral uptake. By integrating forecasts, application-specific databases and models in a single open source platform, S2S AccelNet Hub, we will promote implementation of resilient and adaptive management policies. Evaluations of policy and institutional settings that aid or limit the application of S2S predictions will provide insights for forecast developers to customize the forecasts for better outreach in RCOFs. Successful FAS pilot projects will also promote strengthening industry-university partnership and lay the foundation for accelerating FAS research. The broader impacts of this project stem from interactions and exchanges across the NoN in enhancing the outreach and capacity building of major international forecasting institutions and agencies for active engagement with local/regional sectoral agencies. The databases and models developed through the S2S-Application Hub will promote strong collaborative and synthesis activities. Innovative professional training and mentoring plans such as annual symposia and 3-Minute Thesis presentations with participating universities will improve graduate students’ communication in interdisciplinary settings. Finally, key decision-makers could rely on the improved S2S forecasts and knowledge exchange through the FAS projects and S2S-AccelNet Hub for informed policy-making to develop S2S hydroclimatic forecast based adaptive management strategies that meet the UN’s Sustainable Development Goals. 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

Humans change the behavior of wildfires, storms, diseases, and other disturbances. Altered disturbances can cause vegetation to permanently shift between types, such as from forest to shrubland. However, it is difficult to predict which parts of a landscape are vulnerable to shifts. This research examines how interactions between fires and an emerging plant disease may shift coastal forests to shrublands in the western U.S. This work will identify where and why forests are vulnerable to permanent conversion to inform disease and fire management. The research team will apply findings through relationships with the public, managers, tribal communities, and policymakers. This work will create a network across diverse research institutions to mentor students from underrepresented groups. This project will also support an innovative course that integrates art and science and publicly share a dataset that spans two decades. Although persistent state shifts have been described in many systems, empirical work has largely focused on demonstrating state permanence, rather than determining environmental variation in where transitions are likely. This research integrates an 18-year monitoring network, shrub-tree competition experiments that manipulate soil nutrient dynamics and other resource availability, and epidemiological and forest dynamics models. This integrated approach will: 1) quantify the sensitivity of forest-to-shrubland transitions to repeated fire and disease disturbances; 2) identify biogeochemical and disturbance-related feedbacks that destabilize forests and stabilize shrublands; and 3) examine where and when state shifts may occur across heterogenous, rapidly changing landscapes. This work will quantify the likelihood of persistent state shifts using a focal system that comprises plant traits relevant to disturbance-prone systems globally: coast redwood and mixed evergreen forests impacted by an introduced oomycete pathogen, Phytophthora ramorum. To date, there has been limited experimental evidence that diseases can trigger stable state transitions, despite their acute effects on plant mortality, resource competition, and biogeochemical cycles. This research will generate experimental and simulation-based tests of state shifts mediated by disease, while expanding the scope of a valuable longitudinal dataset in a long-lived forest system. 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 NSF Center for Accelerated Photocatalysis (CAPs) is supported by the Centers for Chemical Innovation (CCI) Program of the Division of Chemistry. CAPs aims to leverage light to achieve challenging chemical transformations, enabling access to sustainable, scalable, and more efficient chemical reaction pathways. Despite the proven role of photochemistry in sustainability and pharmaceuticals, progress in this high-priority area has been slow due to labor-intensive, one-experiment-at-a-time approaches that present significant time and resource constraints. CAPs will fast-track fundamental studies of emerging photo(bio)catalysis in chemical synthesis and reaction discovery by implementing a robotic 'co-pilot.' This ‘co-pilot’ will capitalize on machine learning (ML)-assisted robotic experimentation (self-driving laboratories, SDLs) to augment human knowledge in photo(bio)catalysis. Activities will include open-source reporting for all software and data generated, as well as a Summer Boot Camp for high school students and teachers. CAPs will emphasize engaging undergrad/graduate students with physical disabilities in SDL experiments and the development of short videos and science presentations for public events. Using unique multi-purpose SDLs, CAPs will establish a game-changing research program to thoroughly comprehend and accelerate fundamental studies of emerging photo(bio)catalysis for small molecules. Specific reactions to be investigated include: i) photoenzymatic alkene functionalization (ene-reductase) that departs from libraries of alkyl halides and alkenes and ii) asymmetric dual-catalyst photoreactions in parallel with iii) the accelerated discovery of high-photostability dyes and chromogenic photostabilizers using cheminformatics. The resultant photochemistry knowledge gleaned from explorations in the first research thrust will be extended to achieve hydrotrifluoromethylation using photoenzymatic conditions for the first time, along with developing regioselective arene functionalization chemistry. Baseline and quantitative photochemical parameters will be assessed to formulate reaction conditions leading to successful transformations targeting unique approaches towards preparing valuable small molecules (human-driven scientific discovery). Armed with this information, CAPs will rapidly explore/exploit the high-dimensional reaction space using the existing SDL infrastructure at NC State. Physical- and molecular-based ML scoring parameters will be implemented to quantitatively assess the large body of experimental data generated in the SDL experiments. This will provide critical feedback to the experimentalists (data-driven chemical discoveries). The robotic ‘co-pilot,’ developed and deployed in CAPs, will significantly reduce the time required to find the most suitable photocatalyst(s) and substrate scope(s) for photoenzymatic and photocatalytic transformations that could not be achieved using traditional batch experimentation approaches. In this way, photoactivated synthetic methodologies can be explored to benchmark all relevant parameters impacting autonomous robotic experimentation platforms, ultimately paving the way to newly conceived chemical transformations. 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 research project will establish the first spherical wind-powered rover capable of moving in all directions, thereby enabling an unprecedented level of persistence (defined as the ability to execute a mission without stopping to recharge) and autonomy (defined as the ability to operate without human intervention). Analogous to how modern sailboats leverage lifting sails and keels, centerboards, or other vertically oriented foils to enable upwind motion, Spherical Sailing Omnidirectional Rovers (SSailORs) will use lifting sails and a directionally constrained hull to achieve the same result. Specifically, the lifting sails will enable forward thrust even when moving in an upwind direction. The directionally constrained hull will enable significant lateral resistance when the hull heels (tilts) due to the lateral force from the wind, thereby resisting sideslip. While preliminary investigations of the SSailOR indeed confirm its ability to move in all directions under different slopes, the robust achievement of these capabilities across a wide variety of operating regimes (characterized by different wind and terrain, for example) requires a delicate balance between several features related to both the physical design and control system. Achievement of this balance through a formal physical system and control co-design process represents the centerpiece of the research plan. The resulting designs will be validated through a progressive experimental campaign, including wind tunnel testing, dynamic characterization in a controlled environment, and dynamic characterization in North Carolina’s Outer Banks. The research activities will be complemented by outreach activities with both the Engineering Place at NC State University and the University of Michigan Engineering On-Ramp. To achieve robust omnidirectional mobility and optimize the expected performance of SSailORs, the project will pursue a combined physical and control system co-design process that is centered around a unique characterization termed the stochastic velocity polar (SVP). This characterization statistically quantifies the achievable speed of the rover along any direction relative to the wind direction, over a specified set of terrain types and grades. The SVP will first be parametrically characterized based on longitudinal, lateral, and heel (rotational) characterizations of the SSailOR. After this, a sequential plant-controller co-design process will be used to maximize an objective function that statistically characterizes the scientific information that can be gathered from a candidate design and controller, subject to chance constraints. Following the sequential co-design process, a stochastic reachability-based co-design process will be used to refine the design over a reduced design space. The co-design work will be complemented with an experimental plan that begins with static wind tunnel tests aimed at validating and refining the SSailOR model. This will be followed by controlled dynamic testing, with the primary goal of demonstrating upwind mobility in a constant wind field, and will culminate with testing in the Outer Banks, to demonstrate that upwind mobility is preserved in realistic, time-varying wind conditions and inconsistent terrain. 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

Mitigating the impact of global climate change on sustainable agriculture and, consequently food production depends on our ability to cultivate plants that tolerate increasing heat, drought, and extreme weather events while requiring fewer resources for growth. Advances in both fundamental and applied research have driven key innovations in plant science; however, fundamental discoveries in the lab rarely hold up under dynamic field conditions. Thus, they fall short of meeting the escalating demand for effective translatable solutions. The application of Artificial intelligence (AI), machine learning (ML), and other data-driven approaches to the wealth of data generated by fundamental and applied plant scientists offers potential solutions to this problem. However, cross-domain data analytics remain underutilized due to historical disciplinary silos that limit student training. This National Science Foundation Research Traineeship award to North Carolina State University will, in partnership with Fayetteville State University, train twenty-one (21) doctoral students, including ten (10) NSF-funded trainees, at the convergence of plant science and AI to accelerate the translation of knowledge from lab to field to market. The potential of AI and ML to facilitate translational plant science offers a fertile learning environment for transdisciplinary graduate training. Within this traineeship, collaborative graduate student cohort training approaches will be used, engaging diverse students from plant science, data and computer science, and engineering graduate programs. Cohorts will be challenged to complete user-inspired capstone research projects in partnership with local growers and cross-disciplinary faculty advisor teams over two years. These partnerships, facilitated by on-farm learning experiences, an interdisciplinary orientation boot camp, industry internships, and community outreach, will provide an immersive learning environment that enhances students’ abilities to identify and tackle real world challenges at the intersection of basic and applied plant science and AI. A convergent curriculum will increase student proficiency and core competency in these areas, while also educating students on the societal implications, impacts, concerns, and risks associated with applying AI and ML to agriculture. Research, teaching, and academic partnerships with minority serving institutions will enable the future development of a new bridge to the doctorate training program that increases the presence of those underrepresented in plant sciences and engineering. To address the grand challenge of sustainable agriculture and global food security, this training program will create a diverse, interdisciplinary workforce empowered to engage with industry, grower, and academic partners. The NSF Research Traineeship (NRT) Program will bring together graduate students from multiple disciplines interested in design and engineering for sustainability to solve real-world problems and increase climate resilience. This project will offer students not only core technical education and training opportunities but important soft skills to become inclusive and responsible future workforce. It will foster collaborations and support immersive experiences for trainees and build a diverse community in STEAM fields. 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

A fundamental unanswered biological question with biomedical and industrial relevance is how cells integrate external information from multiple biochemical pathways to enter and exit quiescence in response to stress. Non-destructive measurements of single-cell protein expression in yeast cells have revealed that signaling in quiescence-related biochemical pathways can be highly heterogeneous across genetically identical cell populations as they transition into dormancy, even though they are treated with the exact same stress type and timing. Mathematical modeling has produced remarkable insights into the protein interaction networks that govern the yeast cell cycle and quiescence, however, predicting the transition from proliferation into quiescence at the single-cell level remains unclear under most physiological scenarios. The goal of this project is to develop a mathematical model that recapitulates the heterogeneity in proliferation/quiescence transitions of yeast cells in response to multiple quiescence-promoting stimuli. To accomplish this goal, this project will couple the development of novel scientific machine learning methods, chemical and environmental perturbation experiments, and single-cell protein expression measurements in live yeast cells. This research will address the mathematical challenges of learning and validating mathematical models from heterogeneous high-dimensional time series data to answer significant questions about how signaling pathways govern cell fate and differentiation. The project’s findings will be applicable to quiescence-related phenomena such as chemotherapy-resistant quiescent cancer cells, stem cells that exit quiescence for wound healing, and developmental processes that rely on the ubiquitous stress signaling pathways that will be studied. Research findings will be communicated to the scientific community through conference workshops and minisymposia, and to the general public through the creation of new K-12 outreach exhibits. The proposed work will develop a data-driven mathematical framework to mechanistically explain inter-cellular variability during proliferation-quiescence transitions. Specifically, new deep learning tools will be developed to directly learn differential equation models from multivariate protein expression data collected from individual yeast cells undergoing quiescence in response to a diverse range of biologically relevant stressors. These research efforts will involve the integration of recurrent neural networks, multi-task learning, and novel regularization methods that enable deep learning models to simultaneously learn differential equations from thousands of single-cell replicates of protein expression time series data. Sensitivity analysis methods will be developed in conjunction with these new deep learning tools to enable optimization within a vast space of stress combinations and timing, thereby generating quantitative predictions about which experimental perturbations have the greatest effect on inter-cellular phenotype variability. The application of the new framework to non-destructive single-cell data arising from state-of-the-art experimental setups will shed new light on how coordinated cell division, stress, and metabolic signaling pathways produce intercellular variability in protein expression and quiescence phenotypes observed across species. In addition, this project will provide interdisciplinary training to graduate and undergraduate students, and develop open-source code for application to biological data sets involving perturbation experiments with multivariate time series data. 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

Poly-phase sedimentary basins are those that have undergone multiple phases of deformation and subsidence. Such basins are ubiquitous and increasingly important for hydrocarbon and geothermal resource development. However, traditional modeling methods often fall short in classifying the drivers of subsidence, thermal evolution, resource prospectivity and maturity, and general geometry of poly-phase basins. This research focuses on developing novel modeling methods for unraveling subsidence in poly-phase sedimentary basins, with a focus on the Arctic Alaska basin. This research will develop a numerical modeling code in Matlab that incorporates backstripping, flexural isostasy, and depth-dependent extension in three-dimensions to investigate and segregate multiple subsidence events in the Arctic Alaska basin. Research goals will be accomplished by leveraging an exceptional dataset including seismic data, wells, thermochronology data, and thermal maturity indicators. The model will ultimately be disseminated to the public in a user-friendly package that can be applied to sedimentary basins globally. This research will foster new collaborations between the PI who is an early career female, the USGS, and the data science academy at NCSU. As part of this work the team will develop a short course intended for undergraduate to post-graduate attendees to be run at national conferences free of charge. Traditional subsidence analyses (flexural backstripping, McKenzie stretching) are prolific in basin research, but when used independently these techniques can be insufficient and misleading in poly-phase basins, where overlapping subsidence events convolute the subsidence signal. A growing set of observations in basins worldwide indicate multi-phase, poly-phase, or hybrid basins are commonly mischaracterized. The Brooks Range and Arctic Alaska basin collectively form an ideal location for investigating poly-phase sedimentary basins due to nearly overlapping extensional and convergent tectonism. Despite huge efforts by the USGS to collect data on the Arctic Alaska basin, the relationship between extension and convergence with respect to basin subsidence and heat flow is not clear, and has major implications for thermal evolution, hydrocarbon maturity, geothermal energy prospectivity, source-to-sink dynamics, and interpretations of lithospheric structure. The primary goal of this proposal is to quantify and characterize the spatio-temporal evolution of subsidence and heat flow in the Arctic Alaska basin from the Jurassic to the present. The research will utilize publicly available subsurface and thermal data to constrain a new exportable numerical modeling workflow for investigating poly-phase sedimentary basins that incorporates flexural loading, depth-dependent thermal subsidence, and backstripping. This project will deliver 1) a comprehensive study of subsidence drivers in the resource-rich Arctic Alaska basin and 2) an exportable and user-friendly numerical modeling workflow for poly-phase basins that will facilitate comprehensive subsidence analyses of basins 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 · 2024-08

This project supports travel for early career scientists to attend the conference: “Reducing Uncertainty in Soluble aerosol Trace Element Deposition (RUSTED).” This meeting will bring together an international group of scientific experts from the fields of ocean biogeochemistry, atmospheric chemistry, and modeling to focus on compiling trace element solubility data and to discuss ways to improve the handling of soluble iron in Earth System models. Accurate identification and description of iron biogeochemical processes at the atmosphere-ocean boundary are crucial for confident projections of human-induced effects on the carbon cycle and climate, as well as understanding atmospheric nutrient deposition impacts on phytoplankton. The RUSTED Early Career Researcher Workshop is scheduled to take place from November 10-14, 2024, at CSIR - National Institute of Oceanography located in Goa, India. The workshop builds upon the foundation laid by two preceding workshops, which focused on enhancing the comprehension of iron's influence on ocean biogeochemistry, carbon sequestration, and climate dynamics. The meeting includes a focus on critical questions about iron biogeochemical cycling in the Earth System. Participants will be engaged to explore what simultaneous information would be needed to improve model predictions to better represent climate forcing and feedbacks and what steps must be taken to acquire such information, gain insight, and answer the most critical questions. Ocean biogeochemical models have progressed in treating different dissolved forms, but kinetic descriptions of processes affecting iron solubility after deposition remain rudimentary. One third of the invited researchers will be early career scientists (PhD students, postdoctoral researchers, and young research scientists who received their PhD after 2018). The workshop will facilitate interactive discussions among researchers at various stages of their careers working on different aspects of iron biogeochemistry. This workshop is also sponsored by the Scientific Committee on Oceanic Research (SCOR), a non-profit, non-governmental organization that works to advance international oceanographic research. 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

Cybersecurity is becoming an increased concern among young technology users. However, elementary school teachers often have limited preparation to teach students about cybersecurity. Additional challenges include teachers’ limited time and motivation to effectively incorporate cybersecurity topics into STEM lessons, limiting teachers’ ability to enhance students' knowledge and confidence and spark students’ interest and involvement in cybersecurity. This project is designed to iteratively develop, refine, and test an innovative professional development program that supports teachers to infuse cybersecurity into 4th-5th grade mathematics and science instruction. The project will synergistically merge cybersecurity with mathematics and science content in authentic, real-world contexts to teach topics such as cyberbullying, digital security, encryption/decryption, digital privacy, and digital footprint. This project seeks to enhance cybersecurity knowledge for elementary students across urban, suburban, and rural districts in North Carolina which can be extended to and tested in other states. The project will leverage interdisciplinary curriculum design to transform traditional approaches to mathematics, science, and cybersecurity instruction while providing research experiences for graduate and undergraduate students in mathematics and science education and educational technology. A multidisciplinary research team of mathematics and science education, cybersecurity, educational technology, motivation, and design thinking experts will develop a design-thinking-informed framework and professional development to support teachers’ integration of cybersecurity into mathematics and science lessons. The project team will implement the framework and professional development with 15 4th and 5th grade teachers to collaboratively develop and test exemplar cybersecurity-infused mathematics and science scenarios, studying teachers’ knowledge and self-efficacy in parallel via a mixed-methods use study. The project will conduct an experimental study to scale the approach with 60 additional 4th and 5th grade teachers (30 teachers in the experimental group and 30 teachers in the control group) and their students to measure the impacts of the program on teachers’ cybersecurity knowledge and self-efficacy as well as students’ cybersecurity knowledge. Data collection methods and measures will include focus groups, cognitive interviews, semi-structured interviews, teacher self-efficacy and cybersecurity teacher knowledge and interest, teacher classroom observation, and student cybersecurity knowledge assessment. Data analysis will involve qualitative methods such as the grounded theory approach and thematic analysis, and quantitative methods including descriptive analysis, regression analysis, and multilevel modeling. The project team will consolidate the project outcomes and create a web-based repository along with a teacher-to-teacher and teacher-to-researcher communication platform to disseminate instructional resources and findings free of charge. This Design and Development project is funded by the Discovery Research preK-12 (DRK-12) program, which seeks to significantly enhance the learning and teaching of science, technology, engineering and mathematics (STEM) by preK-12 students and teachers, through research and development of innovative resources, models and tools. Projects in the DRK-12 program build on fundamental research in STEM education and prior research and development efforts that provide theoretical and empirical justification for proposed projects. 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.