Virginia Polytechnic Institute and State University

NSF Awards · FY 2024 · 2024-08

Forests and streams are intimately connected by the sharing of carbon as vegetation and animal biomass, such as leaf litter inputs to streams. Still, the land and the stream are often studied in isolation, which can lead to fundamental misunderstandings of how changes in one system impact one or both systems. Globally, salinization is a current major change in both terrestrial soil and freshwater systems. Salinization can change water quality and drinking water suitability, carbon storage and quality, and plant productivity. Freshwater salinization often begins through terrestrial salinization from human activities like urbanization, agricultural practices, and road salting. Yet, predicting how salinization alters carbon inputs across riparian-stream boundaries is not yet possible. This research will quantify how salinization, which can both subsidize and stress organisms, alters carbon processing across terrestrial-aquatic boundaries, through field work and experiments. The project will also examine the relationship between terrestrial and aquatic salinity in part from the collaborations of crowd-sourced k-12 teacher data. This project will also support three female PIs, mentorship of three graduate and four undergraduate students, support three REU students, and incorporate public participation and awareness through citizen science. This research will identify fundamental principles about how aquatic and terrestrial systems will respond to increased salinization. This project will quantify how sodium chloride inputs to riparian zones and streams interact to alter decomposition, secondary production, gross primary productivity, ecosystem respiration, and net ecosystem production in both terrestrial riparian and aquatic stream ecosystems using experimentally paired riparian-stream mesocosms and a field decomposition study across a sodium gradient. The responses are expected to follow a subsidy-stress model. Sodium chloride (NaCl) should act as a subsidy and increase these processes up to some optimal threshold because Na is a biologically essential nutrient. After which, these processes should decrease as Na becomes a stressor at sub-lethal levels and a toxicant at higher levels. The primary objectives are to 1) measure and quantify field terrestrial-stream relationships using a decomposition study that concurrently measures soil and stream chemistry across a large salinization gradient (electrical conductance ranging from 30-1200 micro Siemens per centimeter), 2) experimentally determine how soil salinization impacts terrestrial-aquatic carbon exchange across a gradient of salinization in novel paired terrestrial riparian-stream mesocosms, and 3) quantify the field-mesocosm relationship to determine the congruence of experimental mesocosm- and field-measured decomposition rates across a salinity gradient. Using a combined terrestrial-aquatic approach is essential to understanding, predicting, and ultimately mitigating negative salinization impacts on terrestrial and aquatic ecosystem structure and function. 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

Cyber SMART is the first cybersecurity IUCRC and the first to combine cutting-edge computer science expertise with advances in the social sciences to address emerging cyber issues, challenges, and opportunities. The Virginia Tech site addition expands Cyber SMART into the telecommunications and federal sectors, supplementing the current expertise in the market segments of financial services, insurance, and supply chain logistics for manufacturing and trade and fraud investigation, detection, and prevention. It increases the depth and breadth of research opportunities and outcomes and training in a range of disciplines and will inform the development of policy, standards, and regulation. The Virginia Tech Cyber SMART site addition objectives are focused on communications security, U.S. government policy related to telecommunications, trust and privacy, visualization, and standards, especially in multimedia emergency communications, trustworthy generative AI for secure system operation, confidential computing, and data governance in the cybersecurity hiring ecosystem. This site brings prowess in mathematics, electrical and computer engineering, business, systems engineering, public policy, and economics to bear, and complements current Center expertise in distributed ledger technology and standards, secure information systems, artificial intelligence (AI), cybersecurity and secure communications, quantum computing, finance/economics, business management, digital identity, cyber law/regulation, policy, psychology and ethics. The Virginia Tech Cyber SMART site addition will leverage the investigator team’s ongoing efforts to broaden the talent pipeline in cybersecurity, serving as primary recruitment tools for Center projects and as platforms for sharing the mission of the Center with potential members. This includes a new socio-technical residential cybersecurity community and an internship fair. The Center involves students in all research projects. The site will increase the current Center impact, benefiting industry, individuals who use products and services, educators and scholars, regulators, and society. The Virginia Tech site addition will contribute to the Cyber SMART project repository which includes research data, code, results, emulators, simulators, and scientific papers. 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

Computer simulations of very complex systems are central to many fields in science and engineering, including mechanical and chemical engineering, aeronautics, astrophysics, plasma physics, meteorology and oceanography, finance, environmental sciences, and urban modeling. However, these simulations are hampered by the limitations of currently available numerical methodologies. Specifically, complex systems are driven by multiple simultaneous physical processes with different dynamic characteristics, e.g., atmospheric chemistry and atmospheric transport. Consequently, different components evolve at different rates, some very fast (e.g., concentrations of chemical tracers) and some very slow (e.g., ocean temperature). Traditional numerical methods are ill-suited to solve complex systems with multiple scales and multiple dynamics. This project develops new numerical algorithms that solve different complex system components with different discretizations and different time steps. This new approach will allow accurate and efficient simulations of complex systems and will positively impact many fields in science and engineering. A novel hybrid time integration framework will be constructed to co-simulate complex systems governed by time-dependent partial differential equations. The particular innovation of the hybrid methodology is that it combines discrete and continuous internal stages during each integration step. The mathematical framework offers local truncation error estimates (unlike operator splitting), and provides solutions that do not depend on the convergence of an outer iteration process (unlike relaxation). It allows us to build methods with a higher order of accuracy than current co-simulation methodologies. The developed hybrid methods will have higher orders of accuracy than current co-simulation methodologies while offering tremendous implementation flexibility. High-quality implementations of the new methods will be made available to the community at large. 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

To meet our future energy needs, new chemical reactions that use carbon dioxide (CO2) and other waste products to produce fuels and chemical feedstocks need to be developed. The use of CO2 as a starting material is challenging because it is a gas and not very reactive. Scientists like Dr. Saouma at the University of Utah develop cheap and readily-available catalysts to speed up these reactions. This research project focuses on developing an understanding of how catalyst structure impacts the catalyst performance. This knowledge allows more efficient and effective better catalysts to be prepared. Dr. Saouma is actively engaged in outreach activities that aim to increase female representation in Science, Technology, Engineering, and Math (STEM) fields. She is an instructor and mentor for the College of Science’s ACCESS program, an intensive summer-long program for incoming freshman women that provides them with a comprehensive view of how energy is produced and used in our society. A survey of high school seniors in Utah indicates that prior to college a gender gap exists in students interested in STEM fields. To help offset this, Dr. Saouma is developing an offshoot of ACCESS that is geared towards increasing the number of rising ninth grade women. participating in science. With funding from the Chemical Structure, Dynamics and Mechanisms-B and the Chemical Catalysis Programs of the Chemistry Division, Dr. Saouma of the University of Utah is developing a fundamental understanding that correlates the thermodynamic properties of a catalyst with catalyst performance for systems that reduce carbonyl bonds. The research measures how ligand and metal identities impact parameters such as hydricity and propensity to add H2 in systems that undergo metal ligand cooperativity and these characteristics are compared through equilibrium measurements. These measurements are also correlated to the catalyst performance in terms of kinetics, mechanism, and scope. Parallel work on developing ligands that are designed to allow separate tuning of the thermodynamic parameters is also under investigation. Dr. Saouma is part of the College of Science’s ACCESS program, whose mission is to increase the representation of women in leadership in the STEM disciplines. In addition to mentoring and instructing the incoming freshman women of ACCESS, Dr. Saouma is developing a sister program that will encompass a summer camp for rising 9th graders, to help address the gender discrepancy in STEM interest that exists amongst high school students in Utah. 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

The US and Japan have a >10-year collaboration on the Japan-US Networking Opportunity (JUNO) Program. This project will support a joint US-Japan workshop to elucidate current challenges in networking research. The workshop will be open to the community and the workshop output will be captured in a publicly-available report. Workshop themes under consideration include: 1) Augmented Network Architectures (Network + X Architecture): The goal of this thematic area is to explore the integration of advanced capabilities, such as computing and artificial intelligence (AI),into the current architecture of wireless and wired networks. 2) Extremely Advanced Networks for Wired and Wireless Communications: The goal of this thematic area is to investigate advanced networking capabilities for enabling wired and wireless communications at scale and with maximum efficiency. 3) Use Cases and Associated Networks: The goal of this thematic area is to exploit emerging use cases that can leverage the capabilities of the first two thematic areas. 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

We are on the verge of the development of the next generation of wireless networks, variously referred to as 6G, Next G, or Future G. Over the past decades, mobile networks have become part of the country’s critical infrastructure, and it is vital for the U.S. to maintain technological leadership in this area. This industry-university research center, WISPER, forms a partnership between industry and academia in the research and innovation that will drive Next G. WISPER stands for Wireless Innovation towards Secure, Pervasive, Efficient and Resilient Next G Networks, reflecting a consensus that the next generation of networks needs to be secure by design, efficient and sustainable in the use of resources, and resilient to both failures and attacks. Our goals with WISPER include: (1) to grow the U.S. competitiveness and innovation capacity with Next G wireless technologies; (2) to deliver breakthrough pre-competitive research for enabling secure, pervasive, efficient, and resilient Next G; (3) to contribute to the emerging North American vision for the Next G, currently coordinated by the Next G Alliance; (4) to guide the R&D effort on Next G; and (5) to train a workforce prepared to tackle complex Next G challenges. Researchers from Virginia Tech, the University of Arizona, and George Mason University will work together with a large array of industry members to develop this technology under the four major themes of: open software and interfaces; artificial intelligence-native network operation; spectrum innovation; and security. Virginia Tech serves as the lead site and will leverage its extensive Next G testbed facilities in this research. WISPER aims to impact the 6G development and pre-standardization activities by leveraging its strong alignment with key 6G stakeholders. Furthermore, WISPER will respond to the urgent need for a skilled Next G workforce. The WISPER leadership has extensive experience recruiting students in computing; to this end, we will leverage the Commonwealth Cyber Initiative (CCI) consortium, which includes three HBCUs and dozens of community colleges. Developing our solutions on open-source platforms, such as Open RAN, will increase the accessibility of advanced Next G techniques, methods, tools, and platforms by professionals, trainees, and students. At Virginia Tech, we will also partner with the VT Center for the Enhancement of Engineering students in WISPER; all WISPER students will benefit from the array of internships and project-based learning opportunities provided through CCI. 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

Recently, the plant genomics field has witnessed a rapid growth of the applications of single-cell RNA sequencing technology--a method that can quantify gene expression in individual cells--in a variety of plant species. To gain insight from this vast amount of expression data on genes and their function in the cells from where it was sampled, this project team will develop new computational tools for mining and analyzing both published and newly generated data. There are several major challenges in analyzing plant single-cell expression data, including (1) determining known and new cell types and their function in species that are not well studied but important for agriculture, and learning plant biology, (2) comparison across species, and (3) a lack of high-quality curated training data for developing computational tools to analyze plant data. To address these challenges, this project team will develop new computational tools for the analysis of single-cell expression data across diverse range of plant species to assess the conservation and divergence and discovering novel cell-types and gene functions. Science outreach and training activities in bioinformatics will include developing teaching and training course materials to engage researchers, undergraduate and graduate students, and high school students. The single-cell transcriptomics is generating a huge amount of data from varied species of plants and is leading the community effort in identifying cellular-level transcriptome events and processes. Many of these events and processes are unique to a cell type, species, or a cell’s developmental stage. The use of wide array of current methods, lack of shared resources, and a common or species-specific set of cell-type markers, is creating a bottleneck for inter and intra-specific comparative analysis and knowledge dissemination. The team proposed, Aim-1: Develop computational tools for multi-reference-based single-cell/nucleus annotation for plants. Upgrade the orthologous marker gene group and develop a method for cell cluster annotation for non-model species along with a data browser for cell cluster visualization and comparison. Aim-2: Develop generative models to improve the resolution of single-cell sequencing data and appropriately analyze data from cells that are not used for training the model. To compare and validate predictions, single-nucleus, and single-cell (protoplast) transcriptome data from a control experiment on tomato will be used. Aim-3: Develop teaching and training course materials on single-cell data analytics to engage researchers in both online and in-person workshops. Plant genomics data analytics and bioinformatics skills training activities will also be developed to engage high school 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 · 2024-07

Nick Mayhall of Virginia Tech is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop quantum-inspired electronic structure theory methods. Computational chemistry has become an invaluable resource for predicting and understanding the microscopic origins of chemical reactivity and structure. While both computer hardware and simulation algorithms have witnessed continued improvements over the years, many chemistry problems remain unanswered due to insurmountable computational costs, a situation which has fueled intense interest in leveraging quantum computation for solving chemistry problems. However, because of the fundamental differences between quantum and classical computers, chemistry simulation methods can’t simply be ported from classical over to quantum devices, making it necessary to design altogether new algorithms for deployment on quantum devices. In this project, Nick Mayhall and his research group will leverage ideas and components found in these new quantum algorithms to develop novel classical algorithms for chemistry simulation on currently available classical computers. The goal is to find ways to make use of these quantum algorithmic advances now, instead of waiting until reliable and accurate quantum computers become available. This work will provide publicly available, open-source software, while also addressing our nation’s need for quantum workforce development. Nick Mayhall and his research group will adapt techniques developed for ‘noisy intermediate-scale quantum’ (NISQ) circuit simulations to make them suitable for accelerating electronic structure calculations on classical computers. This project is divided into 3 main objectives: 1) the development of efficient algorithms and open-source software to compute Heisenberg picture expectation values to accelerate the computation of relevant observables like molecular energy, 2) the development electronic structure methods that exploit the unique aspects of the Heisenberg picture computations, and 3) the application of NISQ error mitigation techniques to improve the classical methods developed. The methods developed in this project will be implemented into open-source software, while providing QIS training to chemistry students and postdocs to continue our efforts to help strengthen the quantum 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 · 2024-07

Crypto-ransomware extorts money from victims by encrypting their files. Ransomware attacks cause severe disruptions to everyday life of many individuals and organizations such as the Colonial Pipeline hack which affected nearly half of the U.S. East Coast gas supply. Studies found that in successful ransomware attacks, 77 percent of the victims were running up-to-date endpoint protection. Thus, existing endpoint cybersecurity solutions for ransomware defense need to be strengthened. There is an urgent need to pinpoint the security gap and improve the effectiveness and deployability of ransomware detection technologies. This project aims to address this gap by developing advanced real-time cybersecurity monitoring technologies. The ability to avoid service disruptions and prevent loss of critical data helps stabilize the modern society that critically depends on the availability of computing infrastructures. To monitor system behaviors efficiently at runtime for ransomware detection, we need to overcome a major obstacle of heavy logging overhead, which includes both runtime slowdown and high storage needs. This project team comprehensively evaluates different logging schedules and systematically measures their impact on detection capabilities in real-world environments. In addition, the team aims to create a set of innovative log analysis algorithms for identifying anomalies efficiently and effectively to drastically enhance the current endpoint intrusion detection capabilities. The proposed work on deployable ransomware defenses helps democratize advanced behavior-based cybersecurity solutions to low-resource high-risk organizations, e.g., local and state governments, hospitals, utility companies, and schools. 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

Network security protocols and standards are crucial for the resiliency and trustworthiness of network systems. However, current practices are unable to meet the security and performance requirements of next-generation mobile network systems. For instance, existing systems primarily rely on centralized public-key infrastructure (PKI) and security functionalities, such as symmetric-key cryptography, access control, and key management, that make such systems suffer from various security vulnerabilities and system performance issues. Moreover, despite the recent post-quantum cryptography (PQC) standardization efforts, significant challenges remain unsolved for designing effective, standard-compliant security mechanisms that overcome the hurdles of centralization. The novelties of the project are to create "PKI and symmetric-key alliances" concepts for enabling distributed, standard-compliant PQC and symmetric encryption algorithms, all with enhanced side-channel resiliency. The project's broader significance is on creating innovative solutions that can achieve distributed trust, resiliency against breaches, and seamless device mobility for next-generation network systems to enhance national security. Furthermore, the project broadly offers new educational and publicly adaptable tools. The research team takes a synergistic approach to designing efficient distributed network security frameworks incorporating secure multi-party computation and decentralized architectures to address the limitations of current practices. The first thrust creates distributed NIST-PQC schemes to build compromise-resilient PKIs and scalable PQ-safe PKI alliances with certificateless credentials. The second thrust strengthens core security services by creating distributed NIST symmetric standards for breach-resilient symmetric-key alliances, forward-secure lightweight ciphers, and privacy-preserving access control frameworks. All tasks consider side-channel attacks and their countermeasures in the context of the proposed distributed schemes. The third thrust conducts a comprehensive evaluation and validation of the proposed techniques with experiments on NSF cloud infrastructures and various hardware platforms. The outreach and broadening participation activities include interdisciplinary curriculum development, and summer apprenticeships for K-12 students. The team will explore industrial partnerships for transition to practice, and build open-source platforms for reproducibility and adoption. 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

Bats provide a model system for small aerial vehicles by virtue of their versatility. They have evolved a myriad of capabilities ranging from traveling large distances, carrying loads as high as their own body weight, to chasing insects in flight with extraordinary speed and agility. All this is made possible by membrane-covered wings manipulated by fingers with about 20 joints which gives bat wings the unique capability to change shapes in support of the desired flight objective at any given time. Current drone technology mostly relies on rotary wings which do not provide much maneuverability and hence cannot navigate cluttered environments. The goal of the proposed work is to learn the relationship between changes in wing shape and the flight trajectory. These goals will be accomplished by measuring the wing motion and trajectory of bats as they fly through obstacles in a tunnel. The wing motions will then be used to compute the aerodynamic flow generated by the wings. By relating wing motion to the aerodynamic forces felt by the wings and to the trajectory of the bat, it is possible to identify the consequential wing motions. The design principles learned from bat flight will benefit society by allowing access to natural and man-made cluttered environments for agricultural, environmental surveillance, and other emerging humanitarian uses. It is proposed to make advances in measurements and geometry reconstruction using high-throughput techniques for recording flight and deep learning methods for reconstructing the 3D space-time bat wing kinematics. The experiments are to be conducted on the island of Borneo which is home to about one hundred bat species that include some of the most maneuverable flyers. High-fidelity computational fluid dynamics (CFD) on central processing unit (CPUs) and graphics processing unit (GPUs) is to be used to calculate the time-dependent turbulent flow field generated by the measured wing kinematics. This is to be combined with inertial forces and moments generated by the wings to predict the six degrees-of-freedom translational and rotational dynamics of the bat to be validated with the measured trajectory. The fluid dynamic events that lead to force and moment asymmetries to effectuate a maneuver will be investigated by isolating dominant events through advanced data analytics. The final objective is to use physics-guided deep-learning techniques to establish causal relationships between dominant wing kinematic traits and changes in trajectory during a maneuver for designing transformational bio-inspired bat-like drones. 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 the development of a back support device that assists users with bending and lifting tasks. Currently, more than 25% of the 80 million U.S. workers must lift objects that weigh more than 50 pounds on a regular basis. As a result, more than 120,000 of these workers sustain lower back injuries each year from repetitive lifting resulting in productivity losses, high employee turnover, and lower quality of life. This technology is a back exoskeleton or exosuit, which relieves loads from the back and reduces the risk of injuries or pain. This technology may be used in occupations such as manufacturing, logistics, nursing and emergency medicine, construction, mining, and retail stores. Each of these occupations has a relatively high rate of back injuries due to the repetitive lifting or lifting of heavy objects required for the job. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a back support exoskeleton that assists users with bending and lifting tasks. The technology is designed to make a lifted object feel between 35-50 pounds lighter to the user's back. The design is completely passive, using no motors or batteries. Instead, it utilizes a lightweight fiberglass leaf spring that stores energy when bending and releases the energy to assist when standing up. The exoskeleton looks and feels like a lightweight backpack with leg straps, weighing only 5.5 pounds, and it can be put on in 15 seconds. The combination of the fiberglass spring and a specifically designed differential mechanism allow users to seamlessly go about their work while the device assists them with lifting tasks, without hindering them during walking and without needing to engage or disengage the exoskeleton. A prototype has been shown to decrease muscle activity by over 30% during lifting, which reduces strain on the back, lowering the risk of injury. In addition, the technology also decreases the energy required during lifting, improving overall user well-being. Compared to other back exoskeletons, this device is simpler yet provides three times more support to the back, which may help to reduce back injuries. 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

A major barrier to research on plant function and to crop improvement is a limitation in methods available for genetically manipulating plants. Techniques currently used include the culturing of plant tissues, the insertion or editing of DNA, and the recovery of whole plants, but each of these steps poses challenges to the degree that they work reliably in relatively few species, and even then may not work well in all varieties of the species. To fill this gap, we propose that the parasitic plant dodder (Cuscuta species) can be used to deliver gene editing molecules to a wide range of plants. Dodder plants live by attaching themselves to the stems of host plants and forming connections to withdraw water and nutrients. The organs that form the connections are called haustoria, and function somewhat similar to the way a mosquito taps into a vein to feed, and dodder is able to transmit a variety of large molecules, including proteins and RNAs, to their hosts. Another key feature of dodder is its ability to connect to an unusually wide range of host species, including the most important broadleaf crops. We will evaluate the ability of dodder to mobilize genome editing molecules into its hosts, with the goal of producing gene-edited seeds. Success in this activity would establish a novel vehicle for genetic modification of plants that is relatively simple, rapid, and broadly applicable. The project will explore multiple possibilities for transferring Cas9 and single guide RNA (sgRNA) between dodder and hosts. Among the possibilities are the movement of these molecules from an easily transformed host, such as Arabidopsis, to result in transformed dodder, or the reverse from stably transformed dodder to result in a transformed host. Given success with these, we will explore the ability for dodder to serve as a bridge between a Cas9-sgRNA expressing donor host and a target host (e.g., tomato). The project has three major aims to achieve these outcomes, including: 1) the stable transformation of dodder (C. campestris) to be used in host gene editing, 2) the development of a dodder protoplast system for rapid screening of gene editing constructs, and 3) the development of a dodder-mediated gene editing system. Preliminary results indicate that Agrobacterium-mediated transformation of dodder is possible, and the procedure will be optimized to enable generation of multiple lines bearing various transgene constructs. Other considerations include identifying promotors for the appropriate expression of gene constructs and optimized targeting for systemic trafficking of Cas9/sgRNA in the parasite-host system. For the outreach goal of this project, PIs will develop a program for refugee students to help them participate in project-related activities and develop their understanding of plant science at the University of Missouri. In summary, the project will leverage the intrinsically engaging topics of plant parasitism, RNA mobility, and genome editing to attract these students to plant science. 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

This planning project will engage the Computer and Information Science and Engineering (CISE) community on millimeter-wave (mmWave) and advanced wireless research to gather feedback and gauge support for a new research infrastructure that introduces the principles of open, disaggregated, and “softwarized” radio access networks to mmWave systems. This planning project will analyze research priorities, platforms, and interfaces to inform the development of an experimental research infrastructure combining flexible radio capabilities and control loops based on Artificial Intelligence (AI) within mmWave systems. The intent is to enable adaptive wireless experimentation over high-frequency systems, offering new opportunities for creating, training, evaluating, and improving mmWave systems on realistic, over-the-air scenarios. The goal of this planning effort is to (1) understand the needs of the CISE community; (2) scope enabling technologies and architectural building blocks; and (3) lay out the design for an adaptive mmWave system that would spur experimental research and development exploring high-frequency bands for 6G and beyond. This planning project will provide an initial assessment of the need and potential for an adaptive mmWave research infrastructure, adopting the principles of open and softwarized radio access networks to mmWave systems. The planning activities involve (1) mapping and interviewing relevant stakeholders, including members and officers of the O-RAN Alliance, the Next G Alliance, and PAWR (Platform for Advanced Wireless Research) facilities; (2) conducting community surveys across academic, industry, and government participants to understand their research priorities, needs, and pain points; and (3) visiting existing wireless testbeds to gather insights into their capabilities and limitations, as well as to identify enabling technologies and architectural building blocks. This project will co-design the research vision and infrastructure architecture with the CISE research community and contribute back by disseminating its findings, potential use cases, and designs to help support and motivate additional research and important standardization and regulatory decisions related to high-frequency bands for 6G and beyond. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-06

This project aims to serve the national interest by enhancing Peer+, a free tool that supports Peer Instruction. When using Peer Instruction an instructor displays a question that students answer individually. The students then discuss the question with nearby peers and refine their answers. Peer+ will add two new ways for students to discuss the question with their peers through (1) a text-chat for answering questions during lecture, and (2) a pseudo text-chat for answering questions after lecture. While there is substantial evidence for the effectiveness of Peer Instruction, preliminary research at the University of Michigan has found that using text-chat during lectures improved learning. Peer Instruction is known to improve retention in STEM classes, especially for students from minoritized groups. Providing new types of peer discussion could further improve retention and thus increase the number and diversity of students who succeed in STEM classes. The research associated with this project will increase knowledge about effective STEM education and approaches that attempt to reduce barriers to adoption of Peer Instruction. Since it can be hard for instructors to find the time to adopt new teaching methods, a summer instructor workshop will be offered, and follow-up support will be provided. The project will investigate (1) the effect of three different modes for peer discussion on learning and student satisfaction at four institutions and in a variety of courses, (2) the effect of the Peer+ tool on student retention, and (3) how instructor attitudes towards and knowledge of Peer Instruction change due to a workshop, follow-up support, and use of Peer+. A design-based research approach will be used, based on theory, and the system will be evaluated in real educational settings. The research will be evaluated using both qualitative and quantitative measures. The NSF IUSE:EDU 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. 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 · 2024-05

Laser lithotripsy is the most common procedure to treat urinary stones. In this procedure, a small flexible endoscope is introduced up the urinary tract to the location of a stone. An optical fiber is passed through the endoscope and the tip is placed near the stone. High-energy laser pulses are delivered through the optical fiber to the tip. The laser energy transmitted to the stone breaks it into small pieces that can pass through the urinary tract. Recently, high-power laser systems have been created that make the procedure easier, shortening operating time and producing smaller fragments. However, the energy from these high-power lasers turns into heat that can damage kidney and other tissues. The FDA and the public health community do not presently have standard regulatory science tools for safety evaluation of laser lithotripsy devices. The objective of this project is to develop advanced tools to be used by industry, researchers and regulatory groups to evaluate heating from these lasers to improve the safety of future devices. This project will perform computer simulations and lab experiments to identify how heating is impacted by different laser characteristics. The project will create a database to identify safe power limits, and guidance documentation for evaluating new devices. It will also involve training future scientists and engineers in regulatory science, and educating physician and medical student groups on these effects. The project will support public health by reducing the risk of serious complications during these procedures and introduce protocols for safe use. Laser lithotripsy is the most common intervention for urinary stones, where a laser fiber is passed through an endoscope to deliver pulsed laser energy causing stone fragmentation. The recent introduction of high-power laser systems has expanded the capabilities of laser lithotripsy. However, higher laser power presents a risk of overheating the calyceal fluid and tissue. The impact of various physical, biological, and operator factors on this thermal effect is unknown. Furthermore, the biological sequelae from thermal injury to these tissues are not fully characterized. The FDA and public health community are lacking standard test tools, test protocols and guidance documents for laser radiation safety evaluation of these technologies. The objective of this project is to develop advanced quantitative regulatory science tools to evaluate photothermal effects to the urinary tract during laser lithotripsy and improve the safety of future devices. A computational finite-element model for laser lithotripsy will be developed to simulate physical processes of laser-induced heating, as well as laser, irrigation, and gravity induced fluid flow within the urinary tract to determine the spatiotemporal distributions of heat and pressure. The models will include realistic parameters of the biological fluids and tissues. The models will be used to simulate clinically relevant scenarios of laser lithotripsy to assess and predict thermal and pressure effects to the tissues, and a database will be defined for relevant limits of the exposure parameters required to produce bioeffects. Finally, guidance documents will be produced for evaluating future devices and exposure scenarios. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.