University Of Nebraska Lincoln

NSF Awards · FY 2025 · 2025-01

The lack of network infrastructure in the Midwest and EPSCoR states has widened the digital and economic divide between rural and urban America. Open radio access network (O-RAN) initiatives have gained significant momentum in revolutionizing, defining, and shaping next-generation mobile networks, including Beyond 5G and 6G. In O-RAN mobile networks, network management plays a critical role in overseeing various aspects of network infrastructure, including service orchestration in non-RT RICs and resource allocation in near-RT RICs. Existing approaches generally rely on offline-train-online-deploy strategies using homogeneous AI/ML agents, which face challenges such as simulation-to-reality discrepancies and non-stationary learning environments in real-world, large-scale networks. The long-term vision of this project is to achieve autonomous mobile networks for 6G by designing novel AI/ML techniques to address real-world network management challenges, including, but not limited to, safety, scalability, robustness, and practicality. The project's outcomes are expected to significantly reduce the operating expenses (OpEx) of current mobile networks, thereby facilitating the widespread deployment and cost-effective operation of mobile networks across Nebraska, the Midwest, and EPSCoR states, ultimately contributing to bridging the digital divide between rural and urban America. This fellowship project outlines a first-of-its-kind safe zero-touch network management system by designing a new safe online hierarchical learning framework for O-RAN mobile networks. Leveraging the city-scale network infrastructure at the host institution, Iowa State University, the project will focus on three research objectives. First, the project will focus on online resource allocation at near-RT RICs using safe deep reinforcement learning. Another focus will be on the online service orchestration at non-RT RICs through robust Bayesian learning. Additionally, the project will conduct extensive field testing and evaluation within the O-RAN mobile network at both the home and host institutions. This fellowship will generate long-lasting benefits for the home institution, the University of Nebraska-Lincoln, by strengthening the PI’s research portfolio, enhancing the existing educational platform, and fostering a collaborative research group. This project holds the potential to revolutionize current practices in the acceptance, adoption, and deployment of AI/ML techniques for managing nationwide mobile networks within the telecommunications industry. 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 2025 · 2025-01

----------------------------------------------------------------------------------------------------------------------------- This award is funded by NSF Global Centers program, an innovative partnership with international funding agencies to jointly support use-inspired research addressing global challenges through the bioeconomy. This project is supported by NSF, the Research Council of Finland, and Business Finland. It supports U.S.-based and Finland-based researchers developing international partnerships and building multi-stakeholder engagement while further developing their project toward a global multinational effort. The global food system is environmentally, economically, and socially unsustainable. Current practices contribute to significant greenhouse gas emissions and resource consumption. They incur hidden environmental, health, and poverty-related costs totaling US$20 trillion worldwide. Importantly, they are insufficient for feeding a growing global population with one in three people suffering from hunger or malnutrition. These alarming outlooks demonstrate an urgent need for nutritious, environmentally and socially sustainable, and economically viable food production. This project - Food Innovation and Diversification to Advance the Bioeconomy (FoodID) - promote an international collaboration among investigators from academia and the industry notably in the United States and in Finland. The vision of FoodID is to transform the future of food where innovative solutions drive a sustainable and resilient bioeconomy. The research focus is on alternative protein and lipid sources from specifically designed plants and microorganisms. FoodID also informs policymaking, enhances consumer understanding, drives economic development, and trains the next-generation workforce. Partnerships with community colleges and minority-serving institutions provide notably training opportunities to students from underrepresented groups in STEM. Taken together, these efforts will improve global food security and create high-quality, sustainable, nutritious food ingredients that meet consumer needs. FoodID is a transformative approach to advance knowledge of plant-based food systems. Leveraging biodiversity and biofoundry practices, FoodID drives innovation and discovery across four research thrusts: (1) a biofoundry approach for manipulation and production of food ingredients in plants and microbes; (2) sustainable biorefineries; (3) novel food design; and (4) environmental and socio-economic discovery. FoodID uses the design-build-test-learn process to develop advanced plant and microbial platforms to produce complex biomolecules. This include using high throughput automated bioengineering and biofoundry approaches to develop and scale new crops and microbial strains. As a secondary focus, FoodID Leverages biodiversity to predictably control complex phenotypes in plants and microbes. The goal is to develop novel ideotypes that direct carbon into high-value functional food components. Sustainable biorefineries, together with novel food design, optimizes the production and utilization of new ingredients, enhancing their functionality. Crosscutting these efforts is the development of new computing methods to improve bio-design, process development, and data management. The team also develops an integrated sustainability assessment model to analyze environmental, social, and economic impacts of value chains. Through these approaches, FoodID serves as international innovation ecosystem driving sustainable food production. 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 2025 · 2025-01

Efficient and compact single photon emission platforms operating at room temperature with ultrafast speed and high brightness are needed as fundamental components of the emerging quantum communication and quantum sensing technologies. However, so far, it has been particularly challenging to design practical deterministic single photon emitters based on nanoscale solid-state materials that meet both the fast emission rate and strong brightness demands. The planned research aims to fabricate metallic nanocavities integrated with hexagonal boron nitride (hBN) flakes with defects acting as nanoscale single photon emitters (SPEs) at room temperature. The proposed project is conducted with a collaborator at Pennsylvania State University. The obtained research outcomes would significantly enhance the performance and efficiency of novel quantum devices development by providing fundamental understanding of the rich quantum phenomena at the nanoscale. The research activities at the host site will help train the lead researcher's graduate students from the University of Nebraska-Lincoln (UNL) in using high-end facilities. Their exposure to cutting-edge research in quantum optics and applied aspects of quantum communication is part of the increasingly important workforce development. This project also supports the research projects within the Nebraska EPSCoR RII Track-1 Emergent Quantum Materials and Technologies Center. Single-photon emitters are building blocks for various quantum technologies including quantum sensing and quantum communication. Significant developments over recent years led to the discovery of a variety of atom-like SPEs in solid-state platforms, such as defect-related color centers in wide bandgap materials (e.g., nitrogen vacancy centers in diamond). Although substantial progress led to understanding and utilizing the quantum properties of SPEs, further advances are severely limited by difficulties in achieving the exact placement of quantum emitters, weak light collection due to the high refractive index of bulk substrates, slow emission dynamics, and large-scale integration. The focus of the project is on nanofabrication of plasmonic nanocavities and integrating them to emerging two-dimensional multilayered hBN material with the goal to create gap nano-plasmons enhanced single-photon emission rates. The proposed work includes: (i) fabrication of hybrid plasmonic-hBN nanocavities at Pennsylvania State University, (ii) study the enhancement effect of the nanostructures on the quantum properties of SPEs at UNL, and (iii) perform finite element method (FEM) simulations to quantify the SPE-plasmon coupling coefficients as function of the nanocavity geometry and dimensions to determine the ideal spatial position of the metallic nanostructures. The made hybrid nanophotonic structures would create a rapid speedup and large enhancement in single photon emission that beats dephasing time and leads to the generation of indistinguishable photons needed for key quantum communication technologies such as quantum entanglement. The lead researcher and students from UNL will be trained on the state-of-the-art research tools at Pennsylvania State University, including nanofabrication, advanced characterization tools, and FEM simulations. 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 2025 · 2025-01

This award is for the deployment of the University of Oklahoma’s Rapid X-band Polarimetric Radar (RaXPol) for an educational and outreach project hosted by the University of Nebraska-Lincoln (UNL) and Nebraska Wesleyan University (NWU). Students will participate in the planning and deployment of the radar for small scientific projects. These types of hands-on activities are acknowledged to produce superior educational outcomes and enhanced interest in the sciences. Additionally, the radar will be exhibited to K-12 students at multiple schools in Nebraska. The 2025 Nebraska RaXPol Education and Outreach (NREO-2025) project has three principal objectives: provide undergraduate and graduate students in UNL’s Radar Meteorology course an opportunity to use a research radar to collect data for micro research projects; improve scientific literacy by giving students in NWU’s Introduction to Meteorology general-education meteorology course opportunities to learn about the scientific method and see examples of its application; exhibit a valuable NSF-supported facility to local K-12 students. Students will identify a scientific question that can be answered with data collected by the radar, develop an experimental design, present their projects, and then execute the projects within an 800km radius of Lincoln, NE, with a focus on deep convective storms. 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 · 2024-11

Engineering education (EER) PhD graduates are increasingly being hired into education research faculty positions in technical engineering departments. These positions present multiple challenges for new EER graduates. One particular challenge is lack of experience with navigating the cultural and expertise differences between engineering education and the technical engineering disciplines. The proposed project is designed to develop three postdoctoral fellows' capacity to span these disciplinary boundaries so that they can solve complex education problems in engineering. While engaging in engineering education research, fellows will grow their "3Cs" boundary spanning competencies of communication, collaboration, and coordination. Ultimately, the outcomes of this postdoctoral program have the potential to inform similar postdoctoral programs, providing invaluable postdoctoral training experiences for future STEM education researchers. The overall aim of this postdoctoral fellowships program is to prepare discipline-based education research (DBER) scholars for independent research within engineering (E) disciplines. DBER-E boundary spanners are individuals who can work across the knowledge and skills gap between those trained in engineering education and those with technical engineering backgrounds. Those trained to be boundary spanners in engineering education research will be uniquely positioned to facilitate collaboration among engineers, educators, and social scientists. The merit of this work is threefold. First, the program is designed to enable three scholars to create their own new, independent DBER-E programs by providing support and resources to engage in both education- and engineering-centered spaces, as they may experience in their future home engineering departments. Second, the fellows have strong potential to contribute to the research areas associated with the DBER-E program at the University of Nebraska at Lincoln that focus on student success and workforce preparation. Third, the program will examine the use of boundary spanning as a means of grounding postdoctoral training, with new materials and strategies for such programs. By integrating boundary spanning theory into postdoctoral development, there is the potential to generate new strategies for fostering collaboration across disciplines, enhancing and accelerating educational research, and advancing the overall quality of engineering education. This project is funded by the Science, Technology, Engineering, and Mathematics (STEM) Education Postdoctoral Research Fellowship Program (STEM Ed PRF) with co-funding from the Improving Undergraduate STEM Education Program (IUSE:EDU) Program. The STEM Ed PRF Program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. The IUSE:EDU Program supports research and development projects to improve the effectiveness of STEM education for all 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 · 2024-10

The broader impact/commercial is based on the development of a vehicle immobilization device to prevent a vehicle from being stolen by someone who does not have the key to turn the engine. Currently, auto thefts primarily are caused by the limitations of key/keyfob-based vehicle immobilizers, such as wireless keyfobs, physical locks, after-market vehicle alarms, which often rely on vulnerable external wireless communications and in-vehicle networks. The proposed technology aims to address this problem by providing anti-theft protection that is physically isolated from these common cyber-attack vectors. Unlike other systems, this technology does not require car owners to carry any additional token, making it more convenient to use. In addition, this technology may be installed in all existing and new gasoline, hybrid, and electric vehicles, providing a universal solution. This proposed device potentially may address the critical issue of vehicle security and benefit all stakeholders within the auto industry ecosystem. This I-Corps project is based on the development of an anti-theft device for vehicle theft protection. The proposed technology is designed to leverage automotive batteries to authenticate drivers and immobilize vehicles using the authentication results. The disruptive nature of technology lies in its utilization of the battery as a sensing and control channel, effectively safeguarding vehicles against common cyber-attack vectors such as external wireless communication and internal in-vehicle networks. The proposed device consists of three key components: two authentication systems to authorize each legitimate driver using battery voltage/current as the identity carrier, an adaptive and thermally-robust power control module to reduce/restore the battery’s power capacity to dis/enable vehicle access, and four important functions to enable an end-to-end vehicle immobilizer that is compliant with current standards, including estimation of vehicle status, detection of weak/faulty vehicle batteries, detection of illegitimate vehicle accesses, and automatic recharging power supplies to relieve drivers from maintenance burden. This technology has the potential for widespread application in various battery/AC-powered systems, offering physical security in an increasingly interconnected world. 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 tremendous growth of wireless data traffic over the past decades is expected to accelerate even more in future due to increasing demands for high-speed wireless connectivity, ubiquitous network access, and end-user experience. Sub-terahertz (THz) communications, defined as above 100 GHz, are envisioned as a key technology to enable the needed wireless terabit-per-second links by leveraging the hundreds of gigahertz bandwidths available at sub-THz bands. A major challenge in sub-THz bands, caused by higher propagation loss with increasing frequencies, is the limited communication distance. An emerging technology that promises to improve wireless coverage is the active reconfigurable intelligent surface (active-RIS) that consumes low power and provides efficient control of the reflected signals in both phases and amplification. Realizing this potential will require substantial research in hardware design and prototyping of wideband RIS operating above 100 GHz, as well as novel communication and network algorithms for active-RIS-aided wideband systems, together with experimental evaluation and validation of such unique sub-THz networks with active RIS. This project focuses on the 142 GHz frequency band as a front-runner for the first sixth-generation (6G) spectrum to be allocated above 100 GHz and a top choice for future Wi-Fi spectrum allocations in the years to come. The project consists of three intertwined thrusts. The first thrust is to design and prototype a wideband liquid crystal-based RIS with a wide angular range of tunable reflection operating at 142 GHz. Starting with a design for passive RIS as the proof-of-concept at this high frequency, an active RIS design will then be realized using amplifier-integrated LC-based substrate-integrated waveguide, enabling high tunability for each RIS element. The second thrust is to design robust and efficient algorithms for optimal control of the active RIS coefficients including frequency-dependent phase shift and amplitude amplification. Novel algorithms leveraging unsupervised graph neural networks and reinforcement learning will be used to capture the underlying network interaction and to provide strong scalability and generalizability. The third thrust is to perform extensive validation using the NSF-funded open-source ray-tracing simulation tool “NYURay” for active-RIS-aided sub-THz channel simulations. In addition, the prototyped passive and active RISs will be used to conduct on-site wireless propagation measurements utilizing the wideband sliding correlation channel sounder to create a site-specific hybrid channel model for RIS-aided communication. Through various education and outreach activities to broaden participation in computing, this project will foster knowledge sharing and contribute to industry and regulatory advancements in THz communications. 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

Data-driven machine learning (ML) components have been deployed in multiple cyber-physical systems, from sensing and perception to planning and control. However, the reliability and safety of such ML-based applications remain the most challenging and significant concern for the industry, users, and regulators. Rigorous effort has been made to develop formal methods for ML-based application certification. Most research focuses on qualitative verification of the safety and robustness of neural networks and neural network control systems. There is a lack of methods that can quantitatively verify the temporal properties of ML-based applications, which has been a problem of keen interest for industrial companies in the automotive industry, as quantitative verification results, e.g., probability of collision, provide richer information for better decision-making and planning of autonomous systems under sensing, perception and actuating uncertainties. This project proposes to continue collaborations with industrial partners to develop a new quantitative verification approach for temporal properties of learning-enabled cyber-physical systems (Le-CPS). The project's novelties are the development of new ProbStar Temporal Logic (PSTL) for specifying complex temporal behaviors of Le-CPS and new qualitative and quantitative verification algorithms for verifying Le-CPS temporal properties. The project's impact is supporting transitioning advanced verification technologies into practice via developing a user-friendly interface and improving documentation, benchmarks, evaluation, and engagement with the broader community. The first research objective is to develop the first qualitative and quantitative verification approach for Le-CPS at the system level based on ProbStar reachability. The exact verification scheme provides the precise probability of a safety probability being satisfied, while the approximate scheme obtains the estimated lower and upper bounds of this satisfaction probability. Notably, the exact verification scheme can also construct and visualize the complete set of counterexamples. The second research objective is to develop ProbStar Temporal Logic (PSTL), a formalism enabling quantitative verification of temporal properties of Le-CPS. To construct ProbStar traces, the investigator will develop depth-first-search Prob=Star reachability algorithms for Le-CPS. Finally, the investigator team will develop a new quantitative verification algorithm for temporal properties by transforming a PSTL formula into an abstract disjunctive normal form (DNF) and realizing it on ProbStar traces. To facilitate the adoption of new verification techniques into real robotic applications, the project will develop a user-friendly interface and Robotic Operating System (ROS) integration interface, which supports ROS message collecting, generating verification and monitoring ROS nodes, and creating modeling ROS nodes. The project team will evaluate the efficiency of the new verification algorithms and tool on well-known benchmarks such as advanced emergency braking systems, learning-enabled adaptive cruise control systems, and real learning-enabled F1Tenth testbed. 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

Consider a world in which any user could safely and reliably employ uncrewed aerial systems (or drones) in their work to help with data collection, interaction with the natural or built environment, and even social interactions at work sites rather than as specialty tools with specialist users. While these systems have become pervasive individually, they lack the fundamental understanding of how to work in teams with each other and humans to accomplish larger goals on job sites. This planning project explores ideas to address the intertwined challenges of communicating amongst users and vehicles in a clear, resilient way to allow safe, reliable interactions in diverse use cases, to distill foundational requirements and constructs for multi-user, multi-drone interactions. This future technology would require work across many disciplines to understand what interfaces are necessary, recognize features of safe interactions between users and across environments, learn how people adopt this technology and how it should adapt to users, codify all of these interactions in robust ways, and develop tests for both the safety and integration of different components. The goal of this planning project is to develop the initial conceptualization, planning and collaboration activities that aim to formulate new and sound plans for large-scale projects in these emerging research areas. It brings together teams of researchers in supporting science, technology and applications to develop the concepts and plans to transform the design of human-drone interactions, enabling safer ubiquity on job sites and overcoming the current limitations of siloed deployment and software. It looks holistically at the human-drone team, understanding components of interaction that are generalizable across contexts, and developing systems which can be used anywhere to enable safer, more efficient teaming. This research planning project focuses on radically improving communications, developing methodologies for close interactions in independent multi-human-robot teams, and tools to support interactions with diverse users in applications such as construction and search-and-rescue. 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

Every day, people use scientific information to make decisions that affect their lives. Consequently, it is critical that the results of scientific research on topics impacting the public are communicated effectively. Data visualization is an important part of scientific communication, yet much guidance on the clear and effective design of data graphics has not been tested among a representative population of U.S. adults. The researchers examine how accurately users interpret the information conveyed in different types of data visualizations. The findings support evidence-based recommendations that aid in the fair and equitable communication of scientific information across demographic groups and subpopulations. This research advances understanding of data visualization as a method of communication and provide comprehensive data on viewer interpretation and understanding of data graphics among the adult U.S. population. The researchers implement an online survey through a web portal to 2,000 respondents from a probability-based, nationally representative panel of U.S. adults. Respondents are shown multiple types of data visualizations with various design elements, such as bar and line charts with varying levels of supporting context and asked questions that measure their understanding and interpretation of the data presented. Survey questions then ask about basic interpretation of the values presented in a chart and open-ended responses about what conclusions respondents can draw about the data shown. The researchers analyze the data using generalized linear models, natural language processing, and text analysis techniques to determine the effect of different elements of data visualization design, such as the type of chart or supporting context, on user understanding, and the extent to which understanding may vary across demographic groups. The project is co-funded by the Science of Science: Discovery, Communications, and Impact and the Advancing Informal STEM Learning Programs. 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 three-year renewal RET Site: Collaborative Research: Research Experiences for Teachers across the National Nanotechnology Coordinated Infrastructure is hosted by the Georgia Institute of Technology, the University of Minnesota, and the University of Nebraska, Lincoln. Nanoscale science and engineering is interdisciplinary and cuts across all science and engineering disciplines. As part of the National Nanotechnology Coordinated Infrastructure (NNCI) this program supports 12 in-service, high school and community college faculty each year. Participants will engage in high-quality, nanoscale science and engineering (NSE) hands-on research in state-of-the-art nanotechnology facilities at NNCI sites for 6 weeks during the summer. Educators will complete a hands-on research project in NSE during the summer with continuing support during the academic year. This RET program spanning three NNCI sites allows participants access to a wider variety of NSE research than would be available at a single-site and exposes participants to the NSE needs of industry and related career opportunities across the nation. Project activities will strengthen participants’ knowledge and understanding about broad educational, industrial, and societal NSE activities and how to motivate their students to explore STEM and NSE fields that may lead and provide them with satisfying and lifelong STEM careers. This three-year renewal RET Site: Collaborative Research: Research Experiences for Teachers across the National Nanotechnology Coordinated Infrastructure (NNCI) is hosted by the Georgia Institute of Technology, the University of Minnesota, and the University of Nebraska, Lincoln. Nanoscale science and engineering is interdisciplinary and cuts across all science and engineering disciplines. The program offers a wide array of topics such as flexible electronics, nanomotors, batteries, environmental filtration, and medical diagnosis of diseases. With support from faculty, mentors, and RET coordinators, the RETs will develop curriculum materials to bring their NSE research back to their classrooms. During the academic year, faculty and mentors will visit the RET classrooms to assist with the implementation and further development of the curriculum modules. This RET program spanning three NNCI sites allows participants access to a wider variety of NSE research than would be available at a single-site and exposes participants to the NSE needs of industry and related career opportunities across the nation. The objectives are to grow a multi-site cohort of educators with research experiences that reflect broad educational, industrial, and societal NSE activities; build and disseminate a library of NSE educational materials; highlight the work of NNCI cohort by attending each sites state science teaching association annual meeting; and encourage RETs to present at professional society meetings. Webinars will be held across all participating NNCI sites to enable teachers to learn about NSE industries and careers as well as discuss their modules. The RET program promotes networking opportunities through participation in on-line presentations and webinars, a Slack group, the yearly state science teacher conferences, professional society conferences, and an in-person convocation. This project is partially supported by the Division of Electrical, Communications, and Cyber Systems, the Established Program to Stimulate Competitive Research (EPSCOR), and the Division of Engineering Education and Research Centers. 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 goal of this project is to develop a novel vehicle theft protection called BVI (Battery-based Vehicle Immobilizer). The project’s novelties lie with the use of 12/24V automotive batteries as a physical channel to monitor and control the interactions between drivers and vehicles. BVI is driven by unending vehicle thefts due mainly to the inability of key- or keyfob-based vehicle immobilizers, which usually rely on vulnerable external wireless communications and in-vehicle networks, to prevent thefts. The project’s broader significance and importance are multi-fold. BVI is to make significant socio-economic impacts by securing vehicles, and thus benefitting all parties in the transportation ecosystem: increasing revenue and boosting brand loyalty for car makers; providing owners/drivers with stronger protection of their vehicles and thus reducing their financial loss and mental stress due to vehicle thefts; facilitating personalized insurance coverage to increase social welfare. BVI’s easy deployability facilitates tech-transitioning, and also offers the project participants (graduate and undergraduate students, including those from underrepresented minorities in computing) multi-dimensional training opportunities and competence. BVI consists of three key research components that are physically isolated from these common cyber-attack vectors, with the main tasks to design (i) two authentication systems to verify each legitimate driver using battery voltage/current as the identity carrier, (ii) an adaptive and thermally-robust power control module to reduce/restore the battery’s power capacity to dis/enable vehicle access, and (iii) four important functions to enable BVI as an end-to-end vehicle immobilizer that is compliant with the IEC 60839-10-1 standard, including estimation of vehicle status, detection of weak/faulty vehicle batteries, detection of illegitimate vehicle accesses, and automatic recharging power supplies to relieve drivers from maintenance burden. BVI is designed as a second-factor authentication solution that is complementary to car keys or keyfobs, and can also be extended to replace them, opening a new era of secure and keyless operation of vehicles. 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

In conventional models of computation, algorithms have access to the complete data set throughout the computation. However, in many modern real-world scenarios, data arrives as a continuous high-volume stream and the processing algorithms do not have enough memory to store the entire data set. The data stream model is a well-studied abstract computational model for handling computations over continuous, high-volume data. This model has become pivotal in algorithm development for large datasets and has significant applications in fields such as data mining, network monitoring, and security. The goal of this project is to study several new and underexplored directions in data stream computing. By involving graduate and undergraduate students in research and mentoring them, the project will contribute to training the next generation of scientists and engineers. This project concentrates on three major research themes: (1) Initiate a study of a new data stream model known as the `right to forget' model. This study is motivated by modern considerations arising due to the explosive growth of data generation as well as privacy concerns. (2) Explore a new and emerging notion of randomized computations known as pseudodeterministic computations in the context of streaming algorithms. (3) Investigate the Delphic set streaming model where each item in the stream is succinctly represented as a set. This investigation is motivated by the recent discovery that connects data streaming algorithms to model counting algorithms--two seemingly disparate research topics. Each of these directions represents a strategic step towards advancing the field of data stream computations, addressing contemporary challenges, and unlocking new possibilities. 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.

NIH Research Projects · FY 2025 · 2024-09

Acute exacerbations, bouts of disease worsening, are common in many chronic conditions like chronic obstructive pulmonary disease (COPD). These exacerbations have drastic impacts on whole person health, and with prompt treatment and intervention can reduce morbidity and mortality. However, determining between an acute exacerbation versus daily variation in disease symptomology is problematic. In COPD acute exacerbations can be objectively detected using patient-reported outcome questionnaires that are completed daily. However, due to daily variation, these tools require 2-3 days to establish a diagnosis. Moreover, daily questionnaires burden patients, making this approach impractical for routine monitoring. The long-term goal of the proposed research is to provide a complementary objective diagnostic measure to facilitate analysis of multicomponent interventions effects on the interconnected physiological systems of the whole person within diverse social and environmental contexts. The research objective is to create and validate a multimodal physiologically-based passive monitoring system and analytic approach based on biorhythm interconnectivity using three specific aims: 1) integrate heterogeneous sensing modalities and extract key features from high-dimensional data; 2) integrate an electronic nose sensor with the wearable device to improve diagnostic accuracy and specificity; and 3) test the hypothesis that biorhythm interconnectivity can distinguish changes in health status as identified by validated patient-reported outcomes (i.e. EXACT-RS and CAT). Complementary and integrative digital innovations designed for remote monitoring of whole person health can improve clinical outcomes by stratifying the risk of exacerbation and offer many advantages, including continuous collection of whole person data, remote monitoring of data by clinicians, and the opportunity to guide multimodality management to improve whole person health. Our technical platform has been designed to be flexible with reconfiguration and integration of additional sensors. This enables utilization of study findings across diverse chronic health conditions including asthma, heart disease, and other inflammatory disorders marked by acute exacerbations necessitating prompt treatment. Our research team that includes a physician, engineer, statistician, bioinformatician, machine learning/artificial intelligence expert, and human movement scientist, is uniquely positioned to successfully complete this research.

NIH Research Projects · FY 2025 · 2024-09

PROJECT SUMMARY/ABSTRACT Early childhood is a formative period for establishing healthy dietary habits and weight trajectories, as such habits and weight predict later health outcomes, and rural children from socio-economically disadvantaged families are 26% more likely to be obese than their urban counterparts, underscoring the need to promote foundational healthy eating habits in rural children to prevent obesity and chronic disease. Given the majority of rural children are enrolled in family childcare homes (FCCHs), these childcare settings are ideal for reaching rural, low-income children and fostering healthy eating habits. FCCH providers can serve as role models, provide repeated exposure and positive reinforcement to choose healthy foods, teach children the knowledge and skills to pay attention to their hunger and fullness signals, and foster healthy food acceptance. National efforts to address childhood obesity call upon childcare programs to implement these responsive feeding evidence-based practices (RF-EBPs); however, their effectiveness is not known, especially in rural FCCHs. The team's EAT for Prevention multilevel feedback engagement model builds rural FCCH capacity to use RF- EBPs and improve rural children's dietary intake. Preliminary studies have shown the feasibility and acceptability of this model, paving the way for the proposed study objective to test EAT for Prevention's effectiveness by conducting a properly powered cluster-randomized trial with 3-5-year-old children (n=200) attending rural FCCHs (n=100). The central hypothesis is that EAT for Prevention will improve dietary and health outcomes among children and improve feeding practices among FCCH providers. The specific aims are to determine the impact of EAT for Prevention on 3-5-year-old children's dietary intake and health outcomes (Aim 1) and on FCCH providers' feeding practices and mealtime emotional climate (Aim 2) and to determine mediators of EAT for Prevention effectiveness (Aim 3). Nebraska rural FCCHs participating in federal food assistance programs will be recruited to reach rural children from low-income families. EAT for Prevention is delivered through Cooperative Extension, and Nebraska's FCCH and Extension systems have characteristics consistent with other rural states, improving the likelihood of rapid and effective dissemination. Extension agents will serve as coaches to provide personalized and targeted feedback to FCCH providers based on their mealtime video observations while addressing FCCH provider challenges and children's eating behaviors. For evaluating the effectiveness of EAT for Prevention with its dissemination in mind, outcomes will be aligned with the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework to assess rural FCCH's implementation of RF-EBPs, changes in children's dietary intake, skin carotenoid and BMI z-scores, and drivers influencing effectiveness. The long-term objective is to improve public health by building rural childcare capacity for addressing the growing problem of childhood obesity in rural America.

NSF Awards · FY 2024 · 2024-09

This project aims to serve the national interest by increasing the number of teachers qualified to teach K- 12 computer science, thereby addressing a growing shortage of in-service teachers. This Improving Undergraduate STEM Education (IUSE) Track 1: Engaged Student Learning, Level 3 project will train undergraduate pre-service students studying to become K-12 teachers to teach computer science as part of their initial teaching assignment, as a significant approach to complement existing professional development programs for in-service teachers and to add more STEM teachers to the workforce. Furthermore, motivated by the trend of increasingly more states, including Nebraska, encouraging or requiring courses in computer science for high school graduation, this project will also study how states can meet the demands of such policies by improving recruitment and development of teachers through statewide collaboration. Notably, the project will guide other states seeking to expand K-12 computer science through undergraduate pre-service education. More K-12 computer science teachers will lead to more K-12 students having access to computer science and thus help broaden participation in computing, particularly for students in rural areas and under-represented groups, for Nebraska and other rural states. This project is guided by the overall vision of building an end-to-end ecosystem, from recruitment and engagement to training and continuous learning for undergraduate computer science pre-service teachers, informed by research and with structures in place for sustainability. The goals of the project are two-fold: (1) addressing fundamental research questions to investigate how to recruit, train, and engage undergraduate pre-service teachers to teach K-12 computer science, and (2) producing tangible products such as curricular designs and infrastructure designs for sustainability, and instructional materials both for preparing undergraduate pre-service teachers, and for use in their future classrooms. This project, titled "Computer Science: Focusing on Undergraduate Pre-Service Teachers, with Unified Research, Ecosystems, and Structures (CS FUTURES)," has four specific aims: (1) develop curriculum and courses for preparing undergraduate pre-service teachers, (2) develop sustainable infrastructures and pathways such as policies and curricula across 4-year and 2-year colleges, (3) develop and investigate a statewide model of recruitment, coursework, and practices for undergraduate pre-service teachers to prepare to teach CS, and (4) disseminate findings and resources. The project team consists of two bachelor's degree-granting and three associate’s degree-granting academic institutions—the University of Nebraska-Lincoln, the University of Nebraska at Omaha, Southeast Community College, Metropolitan Community College, and Western Nebraska Community College—that have been involved in training pre-service and in-service teachers in Nebraska. Although the scope of this project is focused on the state of Nebraska, through the dissemination plans, this project will inform undergraduate education, curricular design, recruitment strategies, and institutional practices for training pre-service teachers across the United States. The NSF IUSE: EHR Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. Partial funding is from the Robert Noyce Teacher Scholarship 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 · 2024-09

Maize breeders have significantly increased crop yields by optimizing plants for higher planting densities. Further improvements in crop productivity per unit of land can be achieved by modifying the structure of individual plants and their arrangement in fields. Our current technologies allow for scanning large quantities of plants, but the acquired data is often underused. The goal of this project is to use the scanned data to create a virtual model of maize (its digital twin), which will capture the maize geometry, reflectivity, and function. The digital twin will be able to simulate real plant growth and response to the environment by careful verification against the measured data. The digital twin will be used in hypothetical scenarios of changing climatic conditions to answer "what-if" questions, providing answers for better plant architecture and planting distributions. By using AI and automatic optimization, this project will attempt to identify genetic markers and candidate genes governing variation in the same traits, enabling efforts to breed or engineer plants with optimal canopy architectures. This innovative approach will advance our understanding of plant biology and contribute to meeting global food demands. This project takes an important step towards in silico optimization of maize canopy architecture. We propose to develop innovative data processing and advanced visualization tools to generate fundamental knowledge applicable to agriculture to advance food needs. Our tools will reconstruct maize into its digital twins (plant ideotypes), simulate configurations of individual plants and plant populations differing in leaf canopy-related traits, and evaluate how plant traits perform in varying environments. We will use the vast amount of gathered data from phenotyping facilities and gantry to reconstruct 3D plants into their simulation-ready digital twins, fine-tune computer simulations to visualize and optimize the plant structure and function and identify optimal canopy architectures for given sets of conditions. This work will be combined with genome-wide association study for leaf canopy architecture traits derived from 3D reconstructions of real populations to identify markers and candidate genes, enabling efforts to breed or engineer plants producing optimal canopy architectures. The results of this work will strongly impact agronomic and plant genetic research in both the public and private sectors. There is a critical need for models to predict how plant varieties will respond to different environments. The 3D interactive application will allow experimenting with complex situations at interactive frame rates on a standard desktop computer, something never achieved before. It will be connected to existing data pipelines that provide vast amounts of (often unused) data. We will develop a set of novel algorithms that reconstruct 3D maize plant shapes and functions from input data from varying sources (RGB, depth, point clouds). The developed system will also generate synthetic data suitable for AI training (labeled sets of plants and 3D geometries with proper lighting). The project will partner with The National Data Mine Network, an NSF-funded initiative and the Computer Science department at Purdue University to engage and recruit students in phenotyping, data analysis, algorithmic design, and deployment. 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

Due to geographic barriers and higher rates of poverty, Indigenous youth living in rural communities have significantly fewer opportunities to engage in high-quality STEM experiences inside and outside of school. Concurrently, schools in both rural and urban settings approach STEM education from a western science perspective, thus limiting opportunities for youth to integrate Indigenous ways of knowing in STEM classrooms. The Intellectual Merit of this Integrating Research and Practice project lies in its aim to co-create a STEM-based informal learning framework that ties together Traditional Ecological Knowledge (TEK) with agroecology. Agroecology integrates ecological, economic, and social perspectives on food systems, and is focused on improving agricultural sustainability through practices including intercropping, organic farming, and soil conservation, all of which are founded in Indigenous agriculture methods. The project will investigate the degree to which the framework supports youth and communities reconnecting with traditional foods and growing practices and promotes their knowledge of sustainability. Food insecurity is experienced by 25% of Native Americans, so by working with Indigenous youth and their communities to rediscover and adopt sustainable agroecology practices this project offers the promise of greater food sovereignty, which can be transformative for Indigenous communities. The learning framework developed and tested by this project could be reused and revised by other researchers and Indigenous communities to engage youth in STEM learning experiences that combine TEK with technology and data science in the service of improving local sustainable food production in both rural and urban settings. This project will iteratively develop an agroecology learning experience at teaching farms for one hundred and twenty Indigenous youth aged 10-18 years, accompanied by fifteen of their community elders, by working with two rural Navajo communities in Arizona and an urban intertribal community in Nebraska. Youth will create food plots with traditional foods and growing practices with the augmentation of networked environmental data sensors (for soil nutrients, light, temperature, relative humidity, and soil moisture) and programmable mechanical systems. In response to community needs and informed by the oral teachings of elders, the youth will design their own agroecology research projects, sharing data-driven growing practices with their communities and upholding traditional food sharing practices. By combining Indigenous research methodologies and community-based design research, the project will address the following research questions: (1) How and in what ways does the preliminary framework support and encourage youth and communities to reconnect with traditional foods and growing practices? (2) To what extent does the integration of TEK and western science promote youth knowledge of sustainability and sovereignty in food production? Evidence will be collected via multiple avenues: interviews, talking circles, documentation of co-design meetings, observations, and youth and community-produced artifacts. 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, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences. 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 NSF Track 4 Broadening Participation in Engineering (BPE) project will establish the Nebraska Engineering Inclusive Excellence Center (NEIEC) within the College of Engineering (COE) at the University of Nebraska-Lincoln (UNL). NEIEC aims to cultivate a diverse engineering workforce by providing education to individuals from varied backgrounds, equipping them with essential technical, professional, and personal skills, and fostering their engineering identity. As the only engineering college in Nebraska, UNL COE is committed to ensuring that its resources and opportunities are accessible to all Nebraskans, including women, students from underrepresented racial and ethnic groups (UREGs), and students from rural areas. NEIEC's mission aligns with NSF’s goal of broadening participation in engineering by prioritizing student success across six core non-technical competencies through the Complete Engineer® program and our values of Community, Impact, and Inclusion in order to develop engineers capable of addressing complex global challenges. The primary goals of NEIEC are to foster a culture of inclusive excellence, reinforce engineering identity, facilitate the development of complete engineers, emphasize inclusive excellence from K-12 recruitment to faculty mentoring, and to examine engineering identity development throughout K-12 education. NEIEC is structured around four pillars. Broadening Recruitment (Pillar I) will build upon and link existing community networks to collaborate with high school educators, inspire engineering mindsets, and bolster engineering identities to increase access to engineering career pathways for Nebraska youth. Enhancing Retention (Pillar II) will scrutinize and address institutional policies that may inadvertently create barriers to persistence toward an engineering degree, particularly among students from underserved groups. This pillar also includes refining and broadening access to the burgeoning suite of specialized student support programs currently offered by the college, while researching how these services contribute to supporting engineering identities and retention. The Complete Engineer® program (Pillar III) will establish pathways for engineering students to achieve the Engagement in within the Inclusive Excellence competency through student grants and faculty-led engagement projects. Research under this pillar will shed light on how student engagement projects focused on inclusive excellence impact undergraduate students’ engineering identity. Fostering Inclusive Faculty (Pillar IV) includes establishing an innovative faculty mentoring program to support faculty in inclusive teaching, student mentoring, and the scholarship of engagement, enhancing faculty capacity to promote inclusive excellence within engineering education. Guiding research questions for this pillar focus on understanding how a dynamic ecosystem promoting collaborative, experiential, and interactive activities can transform engineering culture into a partnership model and serve as a catalyst for shaping strong engineering identities, as informed by Cultural Transformation Theory. Mixed methods-based research data will include questionnaires on elements of engineering culture, student retention data analysis, first-year student surveys, course policy document analysis, focus groups, semi-structured interviews, and analysis of written reflections. The evaluation plan for this project will span three levels, the central and integrative partnership level, the research and education practice (knowledge generation) level, and the participant (including faculty and students) leadership and scholarship level. This project will make significant contributions to the knowledgebase about inclusive excellence in engineering education and fostering a culture that strengthens engineering identity among undergraduate students. NEIEC will produce complete engineers equipped with technical and nontechnical skills who are poised to lead innovation and inclusivity in engineering fields. Its outcomes and best practices will serve as a model for institutions promoting diversity and innovation, thereby contributing to a globally competitive workforce. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This renewal Research Experiences for Undergraduates (REU) Site: Sustainability and Resilience of Civil and Environmental Infrastructure in Rural Areas is hosted by the University of Nebraska-Lincoln. Ten undergraduates each year will engage in interdisciplinary research opportunities focusing on sustainability and resilience in rural areas. Approximately 20% of the U.S. population and over 90% of the land area in the U.S., are integral to the well-being of both rural and urban communities. These areas provide resources for U.S. and global food and bioenergy production as well as the transportation infrastructure from inland urban centers to ports. Two areas characterize the research projects at this Site: 1) environmental and water resources engineering challenges, primarily driven by the agricultural industry and decentralized infrastructure; and 2) transportation and structural engineering challenges, primarily influenced by the low population density and localized “farm-to-market” transportation patterns. Students will be recruited primarily from institutions with limited or no research opportunities and/or students underrepresented in STEM. A series of communication development opportunities including preparation of a conference paper, informal presentations to peers, formal poster presentations, and outreach to high school students will be featured in the professional development component. Participants will also engage in preparation and readiness for pursuing graduate school in STEM areas. Part 2: This renewal Research Experiences for Undergraduates (REU) Site: Sustainability and Resilience of Civil and Environmental Infrastructure in Rural Areas is hosted by the University of Nebraska-Lincoln. Ten undergraduates each year will engage in interdisciplinary research opportunities focusing on sustainability and resilience in rural areas. The projects will illustrate how these challenges are driven by the agricultural industry and a decentralized infrastructure, and how transportation is influenced by patterns of population density and the ‘farm-to-market aspects. In rural areas, both environmental and water resources engineering, along with transportation and structural engineering, face significant challenges. Sustainable and resilient solutions are needed that not only address these unique problems but do so in light of the interconnectedness of these issues. For instance, construction material choices for roadways can impact groundwater quality, while flood management decisions affect the resilience of bridges and structures. The objectives of this REU site include providing participants with first-hand exposure to the civil and environmental engineering challenges facing rural areas through research and professional development opportunities and promoting and sustaining the interest of undergraduate students in persisting in a STEM major and pursuing STEM graduate education. Through this REU site, ten undergraduates per year will be given an opportunity to conduct fundamental research with our faculty mentors. In addition, our REU site will offer a series of communication development opportunities including preparation of a conference paper, informal presentations to their peers, formal poster presentations, and outreach to high school students. This project is jointly funded by the EEC REU and the Established Program to Stimulate Competitive Research (EPSCOR). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Sharing network resources using "network slicing" is the key technique in cellular 5G and beyond wireless networks to flexibly and cost-efficiently support emerging applications with extremely diverse needs, such as augmented reality (AR), autonomous driving, and unmanned aerial vehicle (UAV). With the momentum of open network initiatives (e.g., O-RAN), the network orchestration problem--how to provide needed resources to a dynamic and diverse set of applications--becomes increasingly complex and challenging, due factors such as the high number of network states and the need for nearly real-time control (e.g., milliseconds). Existing coarse-grained solutions cannot tackle this fine-grained network orchestration problem in terms of responsiveness, cost-efficiency, and autonomy, which limits the wide adoption of network slicing by telecommunication network providers. The vision of this project is to achieve network slicing as a service (slicing-as-a-service) with autonomous network orchestration to agilely support arbitrary mobile applications with extremely low costs. This project would advance online network automation in next-generation mobile networks, in terms of autonomy, intelligence, adaptability, and assurance. This project outlines a new autonomous network orchestration framework (AutoSlicing) to achieve pervasive slicing-as-a-service towards next-generation mobile networks. The fundamental idea is to enable deep reinforcement learning (DRL) policies to continually learn to orchestrate and adapt to domain shifting with assured service-level agreement (SLA) of slices via interacting with real-world networks. Specifically, the following research thrusts will be investigated. First, new offline simulator augmentation techniques will be designed to reduce the sim-to-real gap by augmenting existing network simulators. Second, new online network orchestration techniques will be designed to prepare the Deep Reinforcement learning policy for potential domain shifting in real-world networks. The augmented simulator will be used for policy training and evaluation during online network orchestration. In addition, AutoSlicing will be implemented and deployed on an end-to-end mobile network at a site-scale testbed at UNL and a city-scale PAWR platform. 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 National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP) is a highly competitive, federal fellowship program. GRFP helps ensure the vitality and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) and in STEM education. The GRFP provides three years of financial support for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM and STEM education. This award supports the NSF Graduate Fellows pursuing graduate education at this GRFP institution. 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 develop a new, efficient, easy-to-use, and well-justified methodology called ABCel for a purely data-driven statistical inference for many complex models routinely used in natural, engineering, social, and environmental sciences. Examples of such models include phylogenetic trees, dynamical systems, exponential random graph models, etc. Due to the complexity and size of the underlying model classes traditional parameter-based statistical procedures cannot be employed here. The ABCel procedure would draw inferences by comparing the observed data set and multiple new data sets simulated from the model for various values of its parameters. It will require almost no tuning—it could be used off the rack, making it easier to benefit collaborative projects on the spread of diseases (e.g., AIDS, STDs, certain kinds of addictions), monitoring terrorists and similar networks, modeling networks in social media, DEI research, poverty mapping, precision agriculture, and many other fields of study. The project will mentor graduate students, develop course modules, short courses, and several user-friendly software based on the obtained results. The ABCel procedure is a new empirical likelihood-based methodology for Approximate Bayesian Computation (ABC) used for analyzing processes with intractable likelihoods. Such processes allow easy simulation of multiple data sets for any input value of their parameters. However, they behave like a "black box", i.e. because of the complexity and size of the underlying model classes, it is impossible to compute the likelihood of any parameter value. Traditional ABC methods are typically computationally intensive, and not very well-justified. Furthermore, they often require specification of tolerances, smoothing parameters, and distances which crucially affect their performances. For the ABCel procedure, the only inputs required will be a choice of summary statistics, their observed values, and the ability to simulate the chosen summaries for any parameter input. Unlike the traditional ABC methods, no tuning parameters as described above will be required. The parameter posterior will be approximated using an empirical likelihood computed using estimating equations only based on the observed and newly generated summary values. The project will find rigorous justification for the approximation using information theory. Appropriate statistical performance guarantees for the method will be furnished. The team will explore the consistency of the approximate posterior, and its performance under a growing number of samples, replication, and summaries. The procedure will be applied to a detailed analysis of exponential random graph models (ERGM). Such models of social networks are routinely used in epidemiology, sociometry, criminology, national defense, agronomy, small-area estimation, etc. 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

To address the critical need for a more diverse and inclusive engineering workforce, this project will establish a pioneering university-industry-student partnership aimed at facilitating equitable access and transition into civil engineering careers for individuals with disabilities. Despite calls from the National Science Foundation and the National Institutes of Health, people with disabilities remain severely underrepresented in STEM fields. In industry, engineers with disabilities constitute less than 10 percent of the workforce and are less likely to be employed than non-disabled engineers; those who are employed generally experience lower pay. To-date, scholarship examining the accessibility of academic institutions has focused on the programmatic experiences of undergraduate engineering students with disabilities, with little to no work continuing past the point of graduation. As a result, this project, aligned with the National Science Foundation's commitment to fostering inclusivity and innovation in engineering education, represents a pivotal step towards broadening the participation of engineers with disabilities in the civil engineering industry. Focused within the civil engineering sector, pivotal to national infrastructure development, this endeavor will lay the groundwork for transformative programming supporting disabled students' transition from academia to professional practice. People with disabilities have been referred to as “the original lifehackers” due to the innovative ways they alter everyday products, systems, and spaces to access a world not built for them. While innovation and problem solving are core competencies in engineering, the role of people with disabilities as engineers has not been realized for many reasons. These reasons include social and professional stigma and a lack of support structures that facilitate the entry of engineering graduates with disabilities into the workforce. Beyond diversification, the project aspires to promote genuine inclusion, illuminating the underrepresented cohort of disabled engineering students and laying foundational steps for accessible engineering education and practice. This planning grant will contribute to a deeper understanding of existing scholarship and current industry perspectives, provide a framework for developing partnerships between academia and industry, and blaze a trail forward for creating a more diverse and inclusive engineering workforce through the following outcomes: (1) synthesizing relevant literature; (2) identifying and engaging industry stakeholders to explore collaborative tensions and synergies among industry stakeholders; and (3) developing a robust research agenda for the next phases of the project. In Phase 1, we will employ systematic review techniques to conduct a literature review to examine the research landscape of the engineering school-to-work transition, industry practices for hiring people with disabilities, and university/industry partnerships. In Phase 2, we will conduct interviews to help us foster interpersonal relationships with the industry partners recruited in Phase 1. In Phase 3, we will apply the outcomes identified in Phases 1 and 2 to establish a robust research agenda for project continuation. By bridging academia and industry, this research will enrich scholarship, provide a framework for sustainable partnerships, and foster a more inclusive engineering workforce. Moreover, this initiative holds broader impacts by pioneering inclusive career pathways that destigmatize disability in industry, promoting transparency, and emphasizing the unique contributions of individuals with disabilities in infrastructure design. Most importantly, it will provide the critical first steps to creating inclusive and accessible pathways to and through engineering for all engineering 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.

NIH Research Projects · FY 2023 · 2024-09

PROJECT SUMMARY/ABSTRACT Animal research has shown that both the structure and function of the hippocampus change with fluctuations in sex steroids across the female menstrual cycle; however, recent investigations have produced mixed results as to whether measurable changes also occur in the human hippocampus. Most human studies that seek to identify hormone-related brain changes have used volume to assess change; volume, however, is a gross measure that cannot account for microstructural changes that may be occurring. Magnetic resonance elastography (MRE) is an emerging tool for acquiring noninvasive measures of the mechanical properties of biological tissue (i.e., viscoelasticity) providing a measure of microstructural tissue health. The proposed work seeks to (1) investigate tissue viscoelasticity as a neural substrate sensitive to fluctuations in hippocampal microstructure that occur across the menstrual cycle and (2) to identify changes in hippocampal-dependent memory outcomes that accompany ovarian hormone (i.e., estradiol) fluctuations and associated changes in hippocampal microstructure. To address these aims, MRI/MRE, blood, and cognitive data will be collected from naturally cycling women with a typical hormonal profile between the ages of 18 and 40. Blood will be used to confirm periods of low estradiol (i.e., at the start of menstruation) and high estradiol (i.e., just before ovulation) for each individual participant. MRI/MRE scans as well as a cognitive battery designed to assess verbal and spatial hippocampal-dependent memory will then be collected twice from each woman: When estradiol is high vs. low. Based on findings from the animal literature, data analysis will focus on the hippocampus as well as its subfields. It is expected that hippocampal viscoelasticity, particularly in subfield CA1/2, will be relatively high when estradiol is low, and that viscoelasticity will be relatively low when estradiol is high indicating a change in microstructural organization. Further, it is anticipated that hippocampal-dependent memory will vary when estradiol is high vs. low and that the relationship between hippocampal viscoelasticity and hippocampal-dependent memory performance will also differ across these two phases of the menstrual cycle. This work will establish MRE as a useful tool for the study of cognitive neuroscience that seeks to identify subtle microstructural alterations and highlight the importance of choosing appropriate neuroimaging tools when assessing structural changes. Because several critical public health concerns (i.e., cardiovascular disease, depression, multiple sclerosis, Alzheimer’s disease) disproportionately affect women and hormonal fluctuations (particularly estrogen) contribute to the development, onset, and/or progression of many of these disorders, the ability to noninvasively assess the relationship between hormone fluctuation and both neuroanatomical and functional change is essential for implementing effective and targeted treatment plans. MRE is proposed as a tool to meet this need. Further, this demonstration will have broad implications for future science that seeks to quantify subtle alterations in regional microstructure relevant, for example, to mental health outcomes and neurodegenerative disease.