University of Minnesota-Twin Cities

NSF Awards · FY 2024 · 2024-08

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Kenneth Leopold of the University of Minnesota is investigating the strong proton-donating ability of superacids using high resolution rotational spectroscopy. Rotational spectroscopy, when applied to small molecular clusters, provides molecular-level detail about molecular structure and dynamics. However, high resolution spectroscopic studies of proton transfer in small molecular clusters remain largely hindered by the requirement of microsolvent molecules to stabilize the resulting charge separation. Professor Leopold and his students will use superacids to effectively increase the available range of acidities and thus drive proton transfer in clusters that are small enough to study by high resolution Fourier transform microwave spectroscopy. Their studies could provide a better understanding of the interactions that enable chemical reactions in small clusters and could deepen our understanding of solvation, which critically influences thermodynamics and reactivity in solution. This work will have broader impact through the training of students, its potential implications for atmospheric chemistry, climate research, and its exemplification of the value of fundamental research through public outreach. By varying cluster size, as well as the acidity and basicity of the interacting moieties, this work will explore the factors that allow (or disallow) proton transfer in small, jet-cooled acid-base adducts. By using superacids to minimize the microsolvation requirements for proton transfer, the full spectrum of interactions between hydrogen bonding and ion pair formation can be realized within a cluster size range amenable to rotational spectroscopy. Experiments will employ both the older, cavity-style microwave technique of Flygare as well as the newer chirped-pulse method of Pate. The determination of rotational constants, nuclear quadrupole coupling constants, tunneling energies, and internal rotation barriers will provide detailed information about the interactions within the clusters studied. High level computational methods will play a strong supporting role, as they aid in identification of carrier of an observed spectrum and, moreover, provide information such as binding energies and potential surface topology, which cannot be directly determined in these experiments. Thus, theory and experiment are used synergistically to elicit a deeper understanding than either could provide independently. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

Many complex phenomena, such as neural interactions in the brain, weather patterns, heat flow in computing systems, and price fluctuations in financial markets, can be viewed as networks of interacting systems. The behavior of these systems is determined by the individual components and how they interact with each other. Often, the interactions are complex enough to preclude reconstruction of the interaction structure from first-principles. In this proposal we propose to develop data-driven approaches to unravel the interaction structure. However, identification of interaction structure from data poses many challenges that include data collected irregularly in time, unmeasured components, temporal patterns (for example, in the weather) that make data correlated across time, and common physical processes driving the behavior of multiple components. The scientific goal of this project is to devise methods that can correctly identify the links in networks in these challenging, real-world scenarios. This project will transform the methodology and understanding of network reconstruction by showing how to discover network structures with realistic data and model conditions, with provable guarantees. Toward reconstruction of interaction structure, this project will provide results on quantitatively characterizing the accuracy of estimating power spectral densities with respect to the data-size, quantitative end-to-end error bounds on the estimation of mutual information rates between time-series, and methods to provably recover exact low-rank/block-sparse decompositions from temporally correlated data, which are of independent interest. The specific innovations for network reconstruction will result in algorithms with rigorous provable correctness guarantees that are applicable in scenarios with finite data size, linear or nonlinear dynamics, data sampled irregularly in time, unmeasured components, spatial dependencies in driving processes, and statistical non-stationarity. The performance of these methods will be examined in simulations and on real-world time-series data instantiated with computational, power, and weather systems. 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

Advances in optimization have become the cornerstone for the success of many modern applications arising from fields like machine learning, signal processing and control. However, as applications become increasingly complex, simply scaling up the data and problem dimension is no longer sufficient to construct high-quality models. It is also important to jointly model different kinds of hierarchically coupled sub-tasks wherein the solution of one optimization problem builds upon that of others. Unlike standard single-level optimization problems, algorithms tailored for solving hierarchically coupled problems are less explored. Bi-level optimization (BLO) thus draws wide attention across the aforementioned fields due to its power in modeling the complicated hierarchical decision-making process. In this proposal, we plan to study the BLO problems, and in particular, a subclass of it with specific lower-level structures that is challenging and has strong applications in modeling the hierarchical structures arising in modern machine learning and engineering applications such as adversarial learning, inverse reinforcement learning, and continue learning. Specifically, we propose to study the hard instances of BLO where the lower level is nonconvex or constrained, which is much less explored and inherently difficult. This proposal intends to develop a suite of new approaches that not only advance the state-of-the-art BLO literature, but more importantly, can help accelerate the adoption of BLO as a generic tool to model, analyze, and innovate on a wide array of emerging ML applications. Further, we also plan to develop a benchmarking suite that evaluates the state-of-the-art BLO algorithms on relevant ML problems and beyond. This project will further integrate an educational plan with the research goals by i) revamping the existing optimization and ML courses with BLO components; ii) developing a new course project on applying BLO algorithms to ML problems; iii) directly involving undergraduate and graduate students in research, especially from under-represented groups; and iv) outreaching to the general public, in particular K-12 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 · 2024-08

Stochastic gravitational-wave background arises as a superposition of many gravitational wave signals generated by uncorrelated astrophysical and cosmological sources throughout the universe. Measuring the astrophysical component of this background, for example, due to mergers of black holes and/or neutron stars, would provide information about how the Universe's large-scale structure formed. Similarly, measuring the cosmological component of this background would provide unique information about the Universe when it was a fraction of a second old, and about the physical laws that apply to very high energy scales that are not reproducible in laboratories. This project aims to search and detect the stochastic gravitational wave background using data from the ground-based gravitational wave detectors Advanced LIGO, Virgo, and Kagra. The results of the searches will be used to characterize the composition of the stochastic gravitational wave background and to identify implications for different astrophysical and cosmological models. The project will enable the involvement of undergraduate and graduate students in frontier research, and it will promote gravitational-wave science to the general public. Stochastic gravitational-wave background (SGWB) arises as an incoherent superposition of many gravitational wave signals generated by uncorrelated astrophysical or cosmological sources throughout the universe. This project aims to search for and detect the SGWB using data from the upcoming observing runs of ground-based detectors NSF's Advanced LIGO, Advanced Virgo, and KAGRA. Specifically, data will come from the fourth observing run (O4) which will be completed in early 2025, and from the fifth observing run which is expected to start in 2026/2027. Two search techniques will be used. First, the traditional cross-correlation based search is expected to be 10-40 times more sensitive than the most recent results based on the first three observation runs. Second, for the specific case of the SGWB due to binary black hole mergers, the full Bayesian Search will be developed with the potential to improve the sensitivity by ~1000 times relative to the cross-correlation search. Both searches will be sensitive to the high-redshift population of the compact binary systems, complementing the individual binary merger observations and illuminating the formation and evolution of the binaries. They will also constrain cosmological SGWB models (such as models of inflation, phase transitions, and cosmic string models) and therefore probe the physics of fundamental interactions at very high energies, unachievable in laboratories. Both searches will also estimate the directional content (i.e. anisotropy) of the SGWB, hence providing additional means for distinguishing between different models contributing to the SGWB. The project will offer numerous opportunities for graduate and undergraduate students to pursue frontier research, leveraging multiple existing programs to foster the involvement of students from underrepresented backgrounds. The project will also support a series of activities designed to bring the excitement of gravitational-wave science to broad communities in the Twin Cities and Minnesota, including public lectures, physics demonstrations, and presentations in K-12 schools, and others. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

Each year, animals across the world migrate, often traveling thousands of miles. These migrants include grey whales, wildebeest, monarch butterflies and sandhill cranes. And just like a family on a long road trip across the country, an animal on its annual migration has to stop and refuel along the way. Although the time spent at these ‘stopover’ sites is relatively short, it is incredibly important. Migrating animals need to rest and regain energy (by feeding on local animals and plants) before continuing their journey. Migrating animals also play an important role for many local plants during these visits. Migrating animals like birds, bats and insects that feed on nectar can move pollen around, helping plants reproduce. And migrating animals that eat fruit can help seeds within those fruits move to new locations. This project aims to understand how visits by migratory animals affect plants at stopover sites. How do visits by migrants affect how fast plant populations can grow and spread locally? And what happens if migrants stop coming? Will the plant populations cope with the change, or will they decline either slowly or rapidly? This project will use computer-based models to understand patterns. Familiarity with computers and how to create computer code can be a barrier to this type of research for students studying biology in college. So as part of this project, researchers will work with a local (St. Paul, MN) elementary school to expand access to computers and computer programming using robots. In this project, researchers will develop novel mathematical theory to consider both the beneficial services that migrants provide as well as the costs they extract. Each aim will focus on a different type of mutualistic relationship between migratory animals and plants at stopover sites. Aim 1 considers pollination, where migrants consume nectar (reducing energy to plants) while increasing mating (and thus reproduction) through pollen movement. Aim 2 considers frugivory, where migrants consume fruits (reducing plant fecundity) while increasing germination probability (survival) and dispersal for seeds passed through their guts. Aim 3 considers epizoochory, where migrants consume plants and inadvertently pick up adhesive seeds, thus increasing seed dispersal. The outcome of this research will be predictions as to how sensitive plant population spread is to changes in migratory species and when – if ever – loss of migratory species can promote spread, or even cause population collapse. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The broader impact of this Partnerships for Innovation – Technology Translation (PFI-TT) project is to transform the reliability, lifetime, efficiency, and overall utility of electric motors and generators. Motors are everywhere across our societal infrastructure — from transportation and logistics to medical and manufacturing. Uptime, reliability, and energy efficiency are critical concerns. The bearings used in today’s motors limit the performance of the systems and the deployment of more energy efficient technologies. It is estimated that up to 75% of all motor failures are due to bearings. Concerns about further exacerbating bearing lifetime issues often limit the use of variable speed motor drives. Additionally, current bearing lubrication processes pose contamination hazards for many systems. Decarbonization and electrification efforts have elevated these concerns across many industries, leading to product development of oil-free and contact-free bearings, such as magnetic and gas bearings. However, the commercial success of these products has been limited by cost and/or low performance. This PFI project provides a cost-effective solution by using electromagnetism within the electric motor to create shaft forces. The project initially targets industrial compressor systems but promises to revolutionize the entire motor industry. Successful adaptation will enable a 9% reduction in U.S. electric energy consumption. The project addresses challenges that must be overcome to realize cost-effective, oil-free machinery through the use of force-capable electric motors. This technology will be demonstrated at a power scale necessary to justify commercial development. To achieve these objectives, the research plan targets (1) scaling design aspects and assumptions of bearingless motors to the high-power levels required of industrial compressors and (2) creating a low-cost, bolt-on set of electronics that can transform a standard electric motor system into a system that will be capable of creating and controlling shaft forces. A key challenge limiting commercialization of this technology is that all available test data has been conducted at low power levels with inadequate energy efficiency for the markets in greatest need of this technology. The project will leverage recent research advancements in combined winding design and control techniques to improve efficiency and enable the use of standard motor system components. The project will use a professional-grade dynamometer to measure the torque-speed-efficiency characteristics of a 100 kW bearingless motor system composed of a standard motor drive, standard motor with a custom winding, and newly developed control electronics. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The broader impact of this I-Corps project is based on the development of a car-sharing system that not only fosters a culture of communal resource utilization but also addresses key urban challenges. By reducing the number of privately owned vehicles, this initiative can: 1) alleviate parking demands in densely populated areas, freeing up valuable space for alternative uses, 2) mitigate traffic congestion and pollution emissions, and 3) enhance access to affordable and reliable transportation for low-income and underserved communities. Collectively, the system will foster economic empowerment and social inclusion, as individuals can engage in activities that enrich their quality of life. This solution targets customer segments that find vehicle insurance, maintenance, and other ownership expenses burdensome. By paying only for usage time in a shared ownership model, users could reduce transportation costs by up to 65% compared to full ownership. Unlike classic car-sharing platforms, this technology can analyze users’ travel behavior, predict and suggest trips, and maximize vehicle availability. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of an innovative approach in using travel behavior data and predictive artificial intelligence (AI) algorithms for optimizing shared mobility systems. By leveraging these technologies, the commercialized product aims to disrupt the way individual travel patterns are analyzed, predicted, and utilized, leading to more efficient and reliable car-sharing services. Particularly, machine learning algorithms are utilized to collect, analyze, and cluster travel patterns, allowing for a deeper understanding of individual travel needs and predictable future trips. Furthermore, optimization algorithms are developed to match users with complimentary travel needs, maximizing system utilization and ultimately enhancing the reliability and quality of services while reducing the cost of car-sharing. By combining these cutting-edge methodologies and applying them in the context of shared mobility systems, the project offers practical solutions to improve efficiency and address drawbacks of existing mobility services. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The ability of bacteria to swim in fluid media is critical for their survival and proliferation in diverse environments and plays a crucial role in many important natural, environmental and bioengineering processes. As one of the most common types of bacteria, peritrichous bacteria such as Escherichia coli swim by rotating a single helical bundle, which is formed from multiple flagellar filaments growing all over the bacterial body. This proposal addresses a fundamental question in the fluid dynamics of the swimming of peritrichous bacteria, i.e., how do the multiple flagellar filaments of a bacterium synchronize and rotate collectively to provide a coherent thrust, enabling the swimming of the bacterium? Toward solving this long-standing problem, the project will integrate experiments on macroscopic model flagella with experiments on microscopic living bacteria and state-of-the-art numerical simulations. Through a systematic and iterative approach, the project aims to resolve the underlying fluid-mechanics principles governing the complex dynamics of bacterial flagellar bundles and uncover their effects on bacterial swimming. The project will provide good opportunities for recruiting undergraduate students from a minority-serving college in frontier research and for designing scientific demonstrations on bacterial swimming for outreach activities. The goal of this project is to understand the synchronization and collective dynamics of multiple flagella in a bacterial bundle. Particularly, the project aims to reveal how multiple flagella synchronize to form a functioning bundle – an indispensable process for the swimming and chemotaxis of a large class of bacteria – and illustrate the collective dynamics of flagella in the bundle. More specifically, the project will construct the most accurate scale experiments to date with previously unexplored features, which will provide a benchmark to develop an immersed-boundary numerical model for simulating flagellar dynamics at different scales. The predictions of both the scale experiments and numerical simulations will be finally compared with microscopic experiments on real bacteria. More broadly, the work will shed light onto the origin of hydrodynamic synchronization and facilitate the development of engineering techniques for tailoring the synchronized dynamics of micron-sized objects. Beyond the specific scientific and engineering questions, the project will expand the limited toolbox to tackle challenging issues associated with low-Reynolds-number fluid-structure interactions. A versatile experimental platform and a quantitative numerical model will be delivered to the research community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With the support of the Chemistry of Life Processes (CLP) program in the Division of Chemistry, Professor Engelhart from the University of Minnesota-Twin Cities is studying the development of new chemical reactions that are catalyzed by enzymes made of RNA and heme. These reactions form bonds between carbon atoms and halogen atoms. Many RNA enzymes have been discovered, but enzymes that perform such a transformation are a new area of investigation in RNA enzymology. This study could provide insights into the potential roles for this reaction in living systems, as well as in chemical synthesis and biotechnology applications. This project is also integrated into an education and outreach program. This program will engage the broader public through distribution of laboratory kits. It will also engage lifelong learners (age 50+) in a partnership with the Osher Lifelong Learning Institute at the University of Minnesota by introducing them to new aspects of RNA biochemistry through a short course and augmented reality educational tools. This project seeks to investigate a new functional RNA-catalyzed reaction: halogenation by ribozymes that bind heme. In this project, the team proposes to characterize the range of heme ribozyme-catalyzed halogenations that are possible and work towards obtaining new heme-binding ribozymes to better understand which sequences perform halogenation reactions. The project team will use a range of biophysical and molecular techniques, including NMR, ESI, in vitro selection, directed evolution, and next-generation sequencing to characterize the ribozymes. Information gained from this study could provide new insights into the range of chemical transformations that RNA catalysts can promote and the enzymology of these catalysts. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

The focus of this award is the development of a plan for the coexistence at the South Pole of transmissions to and from large communications satellite constellations like Starlink with instruments in the Antarctic Dark Sector vulnerable to these transmissions. This builds on extensive and varied experience in understanding and mitigating interference in precision CMB instruments. The proposed work would also continue ongoing efforts in understanding harmful interference thresholds and developing reasonable and well-justified plans for the inevitable existence of RF transmissions at some level within the Dark Sector. Historically, these efforts have addressed situations as they arise, or after data is discovered to be contaminated. The emerging threat of interference from large satellite constellations is too complex and potentially devastating to scientific datasets to address in the same ad hoc way. The project consists of coordination with the SpaceX network (Starlink) on a plan of coexistence; development of a prototype Starlink terminal suitable for long-term installation, including a winterized remote user terminal; development of an improved RFI monitoring system capable of detecting Starlink transmissions, with visualization tools and integration into scientific data streams; analysis of current data sets from the Dark Sector to characterize and understand RFI issues, and development of standardized RFI susceptibility tests to determine vulnerability of future instruments. The primary focus for this project is instruments (such as CMB-S4) designed to measure the cosmic microwave background with very long integrations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-07

With support from the Environmental Chemical Sciences Program of the NSF Division of Chemistry, Yu Yang of the University of Nevada, Reno and Rene Boiteau of Oregon State University are studying iron complexes derived from lignin-based organic compounds. Iron oxide-associated organic carbon is one of the largest reservoirs of carbon on the Earth’s surface. Predicting changes to the size of soil carbon reservoirs and sustainably managing soil and water quality is an important challenge. A rigorous understanding of the chemical nature of iron-organic carbon complexes has the potential to assist scientists in this endeavor. So far, the origin and formation mechanism of soil organic carbon in complex with iron remain poorly understood. As a primary precursor for soil organic carbon, plant-derived lignin can be degraded into compounds that may bind to iron oxides. This project aims to uncover the chemical nature of lignin-derived ligands for iron. Soil carbon is an important part of the Earth's carbon cycle, and an element of this cycle that is highly relevant to climate. Graduate, undergraduate, and K-12 students will be engaged in this research at both institutions. Leveraging an NSF-funded Innovations in Graduate Education project, a summer camp module will be developed focusing on environmental chemistry relevant to sustainability, to increase the representation of first-generation students and underrepresented groups in graduate studies in science. This project sets out to develop targeted and non-targeted methods to detect and quantify lignin-derived ligands and their iron complexes by liquid chromatography together with high resolution electrospray ionization mass spectrometry and inductively coupled plasma mass spectrometry. The method will be developed and validated with a set of known lignin degradation products and used to characterize the iron-binding products formed by microbial degradation of lignin in pure culture and soil incubation. It is expected that these studies will help elucidate the chemical nature of biopolymer (lignin)-derived iron ligands and their complexes and provide insights into the chemical mechanisms that fractionate soil organic carbon, ultimately governing soil organic carbon stocks. Such understanding is critical for developing predictive models for the nature and form of soil-constrained organic carbon. The methods developed in this project are expected to be broadly applicable for the investigation of other biopolymer (cellulose, cutin, and others)-derived ligands in soil and aqueous 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 · 2024-07

The broader impact of this I-Corps project is based on the development of a tissue-engineered vein valve intended as an effective treatment option for venous ulcer patients. If left untreated, this condition can lead to repeated ulcers and eventual amputation, inflating healthcare costs and resulting in significant patient suffering. Previous attempts at prosthetic vein valves have failed to combat the increased thrombogenicity (producing coagulation of the blood) of the venous system leading to device failure within weeks. The solution explored in this project could be the first device capable of providing a curative solution to these patients, transforming venous patient care. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a novel tissue-engineered vein valve prosthesis that mimics the aligned collagen structure of native leaflets, providing a foundation for mechanical durability and endothelialization of the tissue material. The solution is a bileaflet valve encompassing a nitinol stent which, together with the innovative fabrication technique, is the basis for a filed patent application. The tissue engineered collagen material grown on the stent is a platform technology. 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 · 2024-06

The Earth recycles water and critical elements such as iron through a series of processes related to the creation and modification of tectonic plates. This research focuses on part of the element cycling system: where tectonic plates sink (subduct) into the Earth and heat up, driving chemical reactions among minerals. The reactions release water and other elements. These become part of the continents, atmosphere, and oceans. The research aims to determine the conditions of the reactions. Reactions involving iron, oxygen, water, and other elements affect the planet's composition, and these reactions also affect the planet's habitability. This project uses rocks from places where subducted rocks are at the Earth’s surface. It will determine the composition of two minerals: epidote and lawsonite which contain iron and water. The motivation for the research is the unexpected finding of differences in the state of iron in these minerals and the need to understand the causes and extent of this variation. Two graduate students will be involved (one PhD, one MS) at the University of Minnesota and they will collaborate with scientists in Germany, Australia, and India. The results of mineral analyses in this project add an important dimension to community geochemical datasets. Lawsonite and epidote are abundant minerals in subducted oceanic crust; both are hydrous Ca-Al silicates that carry water and other elements from shallow to deep levels of the planet via subduction. Lawsonite forms under very low thermal gradient conditions and commonly transforms to epidote-group minerals (EGMs) as a result of an increase in temperature during subduction and/or exhumation. This research is based on two key observations: (1) The composition of lawsonite is a sensitive indicator of fluid sources: oxygen isotope values and trace element content (Cr, V, Sr, Pb) signal mantle (serpentinite) vs. sediment as contributors to fluids that participated in lawsonite-forming reactions; and (2) Although commonly assumed to contain only ferric iron (Fe3+), analysis using X-ray Absorption Near Edge Spectroscopy (XANES) has detected substantial ferrous iron (Fe2+) in lawsonite and epidote in high-pressure rocks (blueschists and eclogites), raising questions of what controls the oxidation state of Fe in subducted oceanic crust (pre-subduction rock composition or syn-subduction fluid-rock interaction), whether there are systematic changes as a function of temperature/depth, and whether Fe oxidation state correlates with indicators of fluid sources. This research addresses these questions with an investigation of lawsonite and epidote major, trace, and oxygen isotope composition and Fe oxidation state in rocks recording different pressure-temperature conditions (blueschist, eclogite) and chemical environments (proximity to serpentinite, sediments). The new dataset is a contribution to understanding controls on mineral composition and Fe oxidation state in hydrous minerals in relation to fluid sources and pressure-temperature conditions. Results are relevant to understanding the contributions of oxidizing and reducing sources of fluids that influence subducted plates and subsequently the overlying mantle and, ultimately, continents and volcanic arcs. 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 · 2024-06

Developments in Machine Learning (ML) and Artificial Intelligence (AI) continue to dramatically transform data processing, interpretation, and inference across various domains. These advancements prompt a reassessment of techniques and technologies in signal processing, communications, and networking through a data-centric lens. The forthcoming workshop, scheduled for May 17-18, 2024 at the Twin Cities campus of the University of Minnesota, explores the promises and impact of the integration of ML/AI in Signal Processing, Next Generation (NextG) Communications, and Networking. The workshop will focus on pioneering methodologies to model and govern interconnected systems. It will serve as a platform for interdisciplinary scientific exchange and visioning among field-shaping researchers and practitioners. It will provide opportunities for future collaborations enabling progress in data-driven approaches to signal processing, NextG communications, networking and their applications. The videos of the headline and expert presentations and panel discussions will be made publicly available on the workshop website, and the workshop report will be disseminated. 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 · 2024-06

Energetic electron precipitation (EEP) occurs when the high-energy electrons trapped in Earth's radiation belts enter the atmosphere and collide with atmospheric particles, depositing energy in the atmospheric system. EEP is one of the main processes contributing to the loss of energetic electrons and has important implications in the interconnected atmosphere-ionosphere-magnetosphere system (e.g., changes in atmospheric chemistry, ionization, and conductance) and in space weather (e.g., satellite radiation monitoring, satellite drag, etc.). These energetic electrons are primarily scattered by plasma waves; however, due to limited data coverage, our comprehensive understanding of EEP is limited. In this project, by developing a machine learning (ML) model, the team will characterize and parameterize the EEP phenomenon's properties and dynamics. Modeling the spatial and temporal evolution of regions where EEP occurs will lead to a better understanding of the evolution of the radiation belt dynamics and temporal dependence of the energy input to the atmospheric system. The project is highly interdisciplinary, as our understanding of EEP directly impacts several fields, from the atmosphere and ionosphere system to the magnetosphere, and even potentially provides helpful information for space weather monitoring of electron radiation in the near-Earth environment. The project has the potential to support collaborative efforts across all these communities. The ML model and its outputs will be released to the public, enabling follow-up research projects. The lack of global observations of EEP is a major limiting factor in advancing our knowledge on EEP. The team suggest to parameterize EEP by developing global EEP maps through the use of machine learning (ML) techniques. These maps will be based on measurements from the long-lived NOAA's POES/MetOp satellites and will be produced given a time history of geomagnetic activity. The project will address the following science questions: How does the global electron precipitation evolve in time and space with geomagnetic activity? Which plasma waves correspond to the observed enhanced electron precipitation? How does the improved spatial coverage impact the estimates/constraints on the spatial size, duration, and flux intensity of the electron precipitation regions? Analyzing the spatial and temporal evolution of regions where EEP occurs will lead to a better understanding of the evolution of the radiation belt dynamics and temporal dependence of the energy input to the atmosphere. Additionally, the team will explore whether there is a clear cause-effect correlation between the location and energy of EEP and two main plasma waves. This provides a more definite understanding of the causal relationship between the wave modes and EEP, possibly demonstrating that EEP maps can serve as a proxy for wave activity. Finally, by estimating the size and flux of EEP regions, the team will quantify the electron loss of the outer radiation belt and the EEP input that contributes to variations in atmospheric chemistry and ionization. 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 · 2024-06

The PIs propose a travel grant program to provide support for graduate students from US universities to attend the Midwest Machine Learning symposium (MMLS) 2024 on May 20-21, 2024 . The objective of this grant is to provide travel support for graduate students enrolled in US universities to present posters and participate in the conference. The PIs, representing the conference organizing committee, are requesting to support the travels of up to 20 graduate students for roundtrip airfare and to provide for student lodging up to 150 students for two nights (first come first serve) at the Univ. of Minnesota. The travel grants will be awarded competitively to graduate students from US universities. The awardees will be selected by a committee chosen by the organizing committee and the poster quality will be considered in the selection process. The awards will be administered by University Minnesota, with no overhead to be charged. 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 · 2024-03

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). The East African Monsoon (EAM) provides intense, seasonal rains that are critically linked to food security and infrastructure for a large portion of global population. The timing and amount of these rains are projected to change substantially under anthropogenic climate change. Studying past intervals of global warmth can inform the scientific community and general public on the direction and magnitude of change in EAM characteristics as the planet warms and cools. This project aims to leverage a large archive of fossil soil (paleosol) samples collected from the Baringo Basin of Kenya, Africa, to reconstruct aspects of past hydroclimate and temperature during the Pliocene-Pleistocene epochs (~4.1-2.6 million years ago). This work will produce the first quantitative estimates of precipitation and temperature across intervals of warming and cooling, including the last time that atmospheric CO2 reached current levels. The response of vegetation to these climate changes will be also be documented, thereby informing the scientific community on the sensitivity of different plant groups to changes in climate parameters. This work will support a large cohort of undergraduate student researchers and a PhD student, and scientific results will be incorporated into upper level, data-driven geoscience courses. Paleosols were previously described in the field in the vicinity of a coring locality associated with the Hominin Sites and Paleolakes Drilling Project. New analyses will include reconstructing paleoclimate (rainfall, temperature) using robust, multivariate models based on paleosol bulk geochemistry as well as the clumped isotope composition of pedogenic carbonates; vegetation will be reconstructed using stable isotopes from pedogenic carbonates and organic matter. This work will provide the first quantitative paleoclimate estimates from the Baringo Basin, which contains the most continuous Neogene stratigraphic record in equatorial eastern Africa and preserves a rich paleontological and paleoanthropological archive. The results of this work will be placed within an existing, high-resolution geochronologic framework to test hypotheses that relate the effect of CO2 rise to EAM strength, local climate seasonality, and landscape-scale vegetation structure. The project will include outcrop-to-core comparisons to evaluate proxy robustness, thereby strengthening paleoclimate and paleoenvironmental interpretations. The multi-proxy approach will allow for rigorous testing of proxy robustness and repeatability. 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 2023 · 2023-11

Title: CAREER: Exact Actuation of Magnetic Field Forces Across All Degrees of Freedom in Electric Motors Abstract: Non-technical description: Electric motors are responsible for transporting the world's supply of fresh water, heating and cooling homes and offices, driving critical medical and surgical equipment, and, increasingly, operating transportation systems. The efficiency of this technology is vital in enabling energy sustainability and reducing humanity's carbon footprint. Conventional electric motors rely on bearings to support their shafts. Unfortunately, these bearings have critical lifetime, reliability, and efficiency shortcomings that limit the electrification of transportation systems, the utilization of renewable generation technologies, and the efficiency of fluid handling infrastructure. This project will investigate a new type of motor that does not use bearings. The new bearingless motors will utilize electric current to create controllable magnetic forces that function as set of bearings. The new motors will look and behave in fundamentally different ways that will enable new, extremely efficient, and ultra-reliable systems. This project targets a 9% reduction in US electric energy consumption by enabling new concepts in compressor systems, electrified transportation, renewable energy generation, and energy storage. To translate these research outcomes into the real world, the project incorporates engagement of both the technical community and general public. Beyond the research outcomes, the team's outreach activities will facilitate development of the diverse STEM workforce needed to maintain US leadership in electromechanical power conversion. The team will develop interactive public exhibits of motor and levitation technology and host STEM-enriched experiences aimed at increasing interest and participation in STEM opportunities for middle and high school females and youth from rural, economically disadvantaged regions. Technical Description: The objective of this research is to overcome fundamental challenges of both conventional motor bearings and magnetic bearings. While today's electric motors utilize and control only one degree of freedom (rotation) this project will develop a new generation of electric motors that are precisely actuated in all six degrees of freedom. These new motors will utilize magnetic field forces that are already present within the motor to create a completely bearingless motor that levitates its own shaft. Modelling, control, and design techniques will be developed to unify the science of electric motors and magnetic levitation. The project will use analytic and numeric modeling approaches to create a framework that models the normal and tangential magnetic stresses on the rotor's surface. This framework will be used to determine the required stator currents needed to produce exact force and torque vectors on the motor's shaft. The project will research optimal design of these new motors and test prototypes to validate this new science. The team will develop the science of bearingless motors for both high speed motor systems (industrial compressors and power grid flywheel energy storage) and low speed motor systems (large diameter, rim-driven motors for flight electrification). The outcomes of the research will be disseminated through the standard channels of academic research (presentations and papers) as well as through the construction of a portable bearingless motor prototype that will be exhibited in public demonstrations, conferences, and visits to research institutes. The team will also develop open source, interactive laboratory electronics kits to demonstrate the principles of magnetic levitation and electromechanical power conversion that will be promoted to allow youth to develop county fair exhibits, lifelong learners to explore electromechanical principles, and teachers to adapt the material for their curriculum. 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.