West Virginia University Research Corporation

NSF Awards · FY 2025 · 2025-01

This project aims to serve the national interest by providing research experiences in physics early in the undergraduate STEM pathway. By introducing students to basic math and graphing skills in an applied research environment through a course-based undergraduate research experience (CURE), students are likely to gain new appreciation for the significance of those skills, boosting their future math and physics course performance and retention. Student research projects focus on a new type of magnetism, which provides opportunities to build skills in magnetic and other characterization techniques. Collaborations with investigators at Carnegie Mellon, Rice University, South Florida, and Oak Ridge allow students to pick from a variety of specific topics in the field that may interest them. Students can extend their research projects beyond the classroom and present research results to the broader STEM community. Importantly, research results from student projects have the potential to advance understanding of this new field of magnetism, particularly related to the causes of this effect in various materials. Assessment and evaluation of the proposed research experience can be utilized to develop similar undergraduate experiences for other topics within physics and other STEM disciplines. The educational goals of this program include improving retention/graduation rates, math readiness, and recruitment and to engage students in research earlier in their education. The team seeks to collect a rich set of quantitative and qualitative artifacts to evaluate the project. Retention rates will be compared against historical cohorts matched by mathematics preparation level. Self-efficacy, belonging, and science identity will be assessed both pre and post intervention using research-based assessment instruments. Interviews with students near the end of the semester provide additional context for the quantitative measures. The project engages a three-person advisory board to review progress and recommend improvements for iteratively refining the CURE approach. The work will be disseminated through publications and conferences with physics and physics educators. 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 2025 · 2025-01

The aim of this project is to assess the experiences and obstacles faced by graduate students in science that result from their socioeconomic backgrounds, with the goal of identifying strategies that would help graduate programs and universities better serve and retain such students. Students pursuing an education and career in science can face many obstacles to their success. Some of these obstacles can result from the student’s socioeconomic background. Students from lower socioeconomic backgrounds who enter advanced education and training in graduate science programs face a social and cultural environment that is often dominated by those coming from higher socioeconomic backgrounds. The disconnect between these students’ socioeconomic origins and that of their peers and faculty could lead to feeling like they do not belong within the scientific community. This feeling could create a greater risk of these students leaving their graduate programs abandoning their careers in science and, in turn, harming the growth and innovativeness of the United States’ scientific workforce. Findings and recommendations from the study have potential to broaden participation in the sciences through addressing issues of engagement and involvement. In this project a survey is designed and fielded among a sample of graduate students in five natural and social science disciplines: biology, physics, chemistry, psychology, and sociology. Twenty-five departments for each discipline, varying across program ranking, are randomly selected and a sample frame is built of graduate students in the programs. Students are invited to complete a 15-minute survey and a subset of these are invited to participate in semi-structured interviews to supplement and enrich the quantitative survey data. This project is funded jointly by the Sociology Program and the Science of Broadening Participation Program. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This collaborative project will use field- and model-based experiments to evaluate how different species of tree from different locations on earth take up and store carbon. Cosmic radiation, including particles from the sun, poses a serious threat to satellites, space stations, and human space exploration. Direct observations of extreme cosmic particle events are limited to the last several decades but environmental archives like tree rings and ice cores suggest that rare events can be ~50 times more severe than those documented by the instrumental record. Tree rings are globally common and record past events through isotopic tracers, yet the precision of these records may be affected by both latitude and the way different species take up and store carbon from the atmosphere. Results of this study will create an upper limit on extreme solar events critical for safeguarding space-based infrastructure from harmful cosmic events. This award support early-career researchers, graduate and undergraduate students, and women PIs from ESPSCoR states. Understanding past variability in Solar energetic particle events (SEPs) is critical for space-weather forecasting and safeguarding modern infrastructure. A well-replicated history of spikes in 14C from tree rings would provide an upper limit on severe space-based hazards yielding improvements in forecasting and risk assessment. However, inferences about the timing and magnitude of past 14C production is hampered by uncertainties associated with the role of non-structural carbohydrates (NSC) in the age of carbon allocated to wood. This project uses three of the best-replicated 14C spikes (663 BCE, 774 CE, and 993 CE) as global pulse-labeling experiments to quantify physiological sources of uncertainty on Δ14C measurements from tree rings with the goal of improving estimates of the nature of past 14C production spikes. Using living trees at three sites, the team will determine the age of carbon allocated to wood across three tree life strategies: evergreen conifers, deciduous conifers and deciduous angiosperms. The team will then pair these modern field measurements with new time series of past extreme 14C production events from the same sites and the global dataset of annual tree ring Δ14C measurements, to determine impacts of tree physiology and geomagnetic latitude on estimates of timing, duration, magnitude, and solar phase using a Bayesian framework for modeling 14C production from tree rings. Resolving sources of uncertainty in Δ14C in tree rings will yield better estimates of the magnitude past SEPs and create an upper limit on extreme solar events critical for safeguarding space-based infrastructure from harmful cosmic events. The Solar-Terrestrial Research Program, Paleoclimate Program, and GEO directorate co-fund this project. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Over the past two decades, the use of biometric recognition such as fingerprint or facial recognition, has grown significantly and found applications worldwide due to its ability to provide accurate authentication and convenient user experiences. However, ongoing issues related to privacy and security have emerged, raising concerns within both the scientific community and the general public. Balancing these concerns without compromising system security and performance is a significant challenge. The goal of this NSF CAREER research project is to investigate biometric system security vulnerabilities and develop template data protection mechanisms to enhance the safety and privacy of these systems. The proposed effort also includes educational and outreach activities: Youth Cybersecurity Research (YCR), a program to match interested high school students with faculty with shared research interests; Datathon, an annual full-day focused on the intersection of cybersecurity and biometrics; a student context at a major Biometrics conference; and development of new course materials related to biometric security, for use in both existing and new courses. The goal of this project is to develop a comprehensive and standardized framework for analyzing both hardware and software attacks on biometric systems while addressing issues of fairness and bias. The research focuses on two main thrusts: investigating vulnerabilities and developing robust countermeasures. This project aims to establish a research infrastructure that identifies unexplored vulnerabilities of biometric systems, such as side-channel and hardware fault injection attacks, as well as leakage through bias sources. It also seeks to develop a general evaluation methodology for side-channel attacks in biometrics, including metrics, protocols, and result-reporting procedures. Additional avenues of exploration include developing scalable and universal approaches to enhance existing anti-spoofing biometric techniques and to deepen the understanding of ethical considerations in biometric security. This exploration can lead to advancements in biometric security and contribute to the knowledge base surrounding the protection of sensitive biometric information. 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 proposed STARS-UP (Students Teams of Astrophysics Researchers - Undergraduate Pathways) program will create research partnerships between five two-year community and technical colleges (CTC), four four-year colleges (FYC), including the lead institution West Virginia University, and the Green Bank Observatory. The overarching goal of the research partnerships is to increase diversity, and retention of that diversity, in STEM by creating CTC and FYC partnerships, which will allow CTC students and faculty to conduct original scientific research in a local chapter of a national astrophysics research community while providing them with STEM mentoring. First-generation, underrepresented minority, and other students from low socioeconomic status communities represent the largest untapped STEM talent pool in the U.S. STARS-UP will build a sustainable pipeline to careers in astronomy, physics, and the greater STEM enterprise for students who begin their post-secondary education at community or two-year colleges and transfer to four-year colleges. The project will include collaboration between the NSF-funded Pulsar Search Collaboratory, North American Nanohertz Observatory for Gravitational Waves (NANOGrav) Physics Frontiers Center, and the First2 Network, West Virginia’s NSF INCLUDES Alliance, and will build on several successful education programs. STARS-UP will pair each CTC with a nearby FYC to form bilateral research and mentoring partnerships. These CTC-FYC pairs will work together on a research project throughout the academic year. Project activities will include online training sessions with all participants, local meetings between each CTC-FYC pair, a summer workshop at the Green Bank Observatory, attendance at NANOGrav meetings, and development of faculty and peer mentoring networks. Multiple levels of research projects will be available to students as they progress through the program, ultimately leading to publishable research papers. The project also provides a pathway for CTC faculty to become full members of NANOGrav, hence ensuring sustainability of the STARS-UP initiative. Through involvement with large scientific organizations, the network will be offered as a model community. The project will also include examination of the cumulative impact of participation on retention, transfer, and graduation rates for STARS-UP students compared to historical STEM student data from each CTC institution as well as the impact on STEM interest, STEM belonging, and STEM and cultural identities of participating 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

This project will contribute to the national need for well-educated scientists, mathematicians, engineers, and technicians by supporting the retention and graduation of high-achieving, low-income students with demonstrated financial need by bringing together three small rural primarily undergraduate universities in northern (Fairmont State University), central (Glenville State University) and southern (WVU Institute of Technology) West Virginia. Over its one-year duration, this Collaborative Planning Grant project will lay the foundation for a larger multi-institutional effort to increase the number of talented low-income STEM students in Appalachia who graduate with STEM degrees and secure jobs or pursue graduate education. This project leverages an existing collaboration with the First2 Network, which engages in improvement science to test and scale high impact practices to increase persistence in STEM. The project will contribute to the STEM education knowledge base by conducting a needs assessment tailored to primarily undergraduate institutions (PUIs), utilizing student input and experiences to build a framework that can inform other rural schools across the state and country interested in forming similar partnerships. The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. Four specific objectives guide the project team's efforts toward that goal. First, is to assess the barriers to success for talented, low-income students and design approaches to address those challenges. Second, is to investigate the most effective strategies to recruit S-STEM students and award scholarships. The third objective is to determine common curricular and co-curricular interventions and build a regional learning network of faculty, students, and student support staff on collaborating campuses. Fourth, and finally, is to build capacity to create a successful multi-institutional S-STEM Track 3 project. Over the course of this planning period, project leads will support teams of faculty, student support staff, and students. Their perspectives will guide changes in curricular and co-curricular supports, campus climate, and the financial aid process. The project team will use institutional data and interviews with STEM students to develop a needs assessment report. The data generated and approach to developing the needs assessment will be shared within the First2 Network and the National INCLUDES Network, assisting others in establishing similar collaborations. This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, graduation, and academic/career pathways of low-income students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

Radio pulsars – rotating neutron stars with beamed emission observed as periodic radio pulses – are uniquely powerful tools for probing open questions in “fundamental physics.” Such questions that pulsars may answer include: are there gravitational waves (GWs) at low frequencies, perhaps from the early universe? How does matter behave in the extreme-density environments within neutron stars? Can the effects imprinted on pulsar signals from the interstellar medium (ISM) further probe the environments and properties of neutron stars? A research team at West Virginia University will try to address these and other questions by increasing the capabilities of the NSF-funded North American Nanohertz Observatory for Gravitational Waves (NANOGrav), which uses observations of millisecond-period radio pulsars as a galaxy-sized detector of gravitational waves (GW). The project will incorporate into NANOGrav data from the Canadian Hydrogen Intensity Mapping Experiment (CHIME) telescope. Students working on the project will undertake studies of pulsar timing, ISM, and noise properties that together form the bedrock of a firm NANOGrav detection of GWs. In addition, the investigators will develop a series of open-source, educational activities regarding the searching for and timing of pulsars to help train people interested in pursuing academic study of radio pulsars. The rotational stability of pulsars allows them to serve as precisely-ticking clocks in extreme environments where sub-microsecond-level effects predicted by Einstein’s general relativity can be resolved. Most of the key measurements in gravitational and nuclear astrophysics have come from studying pulsars and their timing properties. The recent evidence for GWs at nanohertz frequencies, recently established by NANOGrav, have shown that the “discovery space” of GW astronomy and neutron-star physics remains largely unexplored. The proposed work leverages these facts in order to build a research program focused on low-frequency GW astronomy and, by the nature of NANOGrav analysis, high-accuracy pulsar science. The resultant studies supported by this grant proposal will: characterize the stability of pulse morphology for all NANOGrav pulsars and thus the noise properties for a GW detection; yield a large number of new neutron-star mass measurements using the maturing framework of scintillation theory; and derive new and unique 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

Given the persistent challenge of racial inequity in STEM, there is a clear need for new models that spur and sustain racial equity change. Successful departmental team-based change efforts demonstrate that change can be created and sustained at the meso level of an institution (i.e., departments, centers, and units as the focus for change). This project will bring together experts in institutional change and experts in advancing racial equity with the goal of combining existing, well tested change models to produce a new, racial equity focused model of change in higher education—the Equity Departmental Action Team (EDAT) model. This model will focus on shifting departmental cultures in ways that benefit, and are grounded in the experiences of, those with historically marginalized racial and ethnic identities. This project will advance the scholarship of racial equity by developing, testing, and refining the EDAT model with STEM departments at a Minority Serving Institution and disseminating the model through partnership with national higher education associations. This project will take place in two major phases: 1) development of the Equity Departmental Action Team (EDAT) model, and 2) pilot of the EDAT model in STEM departments at a Minority Serving Institution, the University of Colorado Denver (CU Denver). The development of the new EDAT model will draw from existing change programs, including the Departmental Action Team (DAT) model and the Dialogues and Change Agent programs. It will integrate multiple theories from systems change, social justice change, social psychology change agency, and intergroup contact. Research activities will focus on both the process and impact of the EDAT model. The project will use surveys, focus groups, interviews, and participant journaling to explore the following research questions. RQ1: To what extent do Foundational Experiences prepare EDAT members for racial equity work? RQ2: What strategies do EDATs deploy when engaging in racial equity work? RQ3: To what extent do EDATs integrate racial equity into departmental culture? Research and program evaluation will be conducted simultaneously with the EDAT implementation so the model can be iteratively refined throughout the project. Dissemination of the model will take place in collaboration with partners from the American Association of Colleges and Universities and the Coalition of Urban Serving Universities - Association of Public and Land-grant Universities. This collaborative project is funded through the Racial Equity in STEM Education activity (EDU Racial Equity). The activity supports research and practice projects that investigate how considerations of racial equity factor into the improvement of science, technology, engineering, and mathematics (STEM) education and workforce. Awarded projects seek to center the voices, knowledge, and experiences of the individuals, communities, and institutions most impacted by systemic inequities within the STEM enterprise. This activity aligns with NSF’s core value of supporting outstanding researchers and innovative thinkers from across the Nation's diversity of demographic groups, regions, and types of organizations. Programs across EDU contribute funds to the Racial Equity activity in recognition of the alignment of its projects with the collective research and development thrusts of the four divisions of the directorate. 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

Engineering identity is the way students describe themselves and how they are perceived as engineers. This identity is related to student retention and success in undergraduate engineering programs. Integrated support programs can improve retention and graduation rates in the science, technology, engineering and mathematics (STEM) disciplines. Student support services include academic, faculty interaction, extracurricular, peer-interaction, professional development and additional support. The levels of support attributed to these areas likely change over the course of study for an engineering student at a large, regional university. Transitions from higher support in a general engineering program in the first year into fewer supports in specific engineering majors in the second-year present challenges that need to be explored. This project will examine connections between engineering identity and the role of student support services. This project is aligned with the goals of the Professional Formation of Engineers: Research Initiation in Engineering Formation (PFE: RIEF) program. A better understanding of the relationship between student support and engineering identity may help support colleges and universities in defining the support structure for improved retention of the next generation of STEM professionals. The long-term goal is to contribute new knowledge into the development of engineering identity as students progress through an engineering program, moving from a general engineering program into a specific engineering discipline. The objective of this project is to evaluate the influence of key student support services variables on changes in engineering identity. The hypothesis is that the overall decreases in student support during the transition from a general first-year engineering program into their chosen engineering major may influence engineering identity. An explanatory cross-sectional mixed methods design will be used to address three research questions: 1) To what extent do student services support factors influence engineering identity over a four-year engineering program? 2) How does students’ engineering identity shift due to changes in support over time in their engineering programs? and 3) How do themes mentioned by instructors, advisors, and mentors help explain any differences? First, a survey of undergraduate engineering students will investigate students’ perceptions about student support services (i.e., Lee et al. 2022) and engineering identity (i.e., Godwin 2016) at various stages in an undergraduate civil engineering experience: 1) first year in a general engineering program, 2) second year after moving into the civil engineering major, 3) third year civil engineering student, and 4) fourth year civil engineering student. Next, a series of student and faculty interviews will examine perceptions of engineering identity and student support services. Finally, interview data from instructors, faculty mentors, and advisors will be analyzed to identify emergent themes related to student support and engineering identity. Results will seek to explain student perceptions from the perspective of those educators who are responsible for designing the undergraduate curriculum and co-curriculum (e.g., faculty, instructors, advisors, and other mentors). These educators can directly utilize the results to inform a support plan for students throughout their program. The rationale for this project is that its successful completion will identify student support variables that influence perceived engineering identity during different stages of an undergraduate engineering program. This new knowledge may aid colleges and universities in developing or refining the support structures necessary to improve retention at their institutions. 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

Flash floods impact communities throughout the US each year, causing loss of life, property, and livelihoods. Rural communities, especially those in the Appalachian region, are particularly vulnerable to flash floods. This, in part, is due to the limited infrastructure to understand, predict, and prepare for flash floods in these regions. To address these challenges, the project will bring together civil engineers, environmental scientists, and social scientists to work alongside community research partners from the region. A key outcome will be an improved ability to understand, predict, and prepare for flash floods under different conditions. This will be achieved with new models, strategically placed sensors, regional flood analyses, and insight from those most affected by flash floods, community members. Researchers and community members will work together to identify specific issues related to flash floods, such as flooding knickpoints and locations where models may perform poorly. By integrating engineering, environmental science, and social science, this project will create solutions tailored to community goals, serving as a model for resilience planning in vulnerable communities across the US. The project's workforce development plan will guide over 500 middle and high school students in the Appalachian region through college and into their careers. Activities will include field experiences, tree plantings, and environmental sensor trainings.. This plan will be put into action with the help of community partners throughout Appalachia, including local citizens, non-profit organizations, and watershed associations. Flash flooding has caused the highest number of fatalities of any flood type in the last two decades. Communities in central Appalachia are especially vulnerable to flash floods. The goal of this project is to gain fundamental knowledge of flash flooding under a variety of weather events and mitigate its impacts in vulnerable rural communities by advancing research capacity, interdisciplinary collaboration, and scientific literacy across Kentucky and West Virginia EPSCoR jurisdictions. Using increased hydrologic research infrastructure and an evidence-based community engagement model, the project will integrate three research tasks to meet this goal: 1) advance the hydrologic sciences to understand controls of flash floods in disturbed and forested stream systems; 2) facilitate community-engaged research to increase resilience and flash flood technology uptake; and 3) develop a community-led science model for increasing knowledge of flash floods. The project will couple catchment-scale hydrologic models (process-based, machine learning), on-the-ground data collection, regional flooding analysis, and hydrologic sensing technology with evidence-based participatory action research to co-create new flash flood knowledge, tools, technology, and subsequently, tailored solutions. The project will provide insight on heavily disturbed landscapes across the US; how to measure, monitor, model, and predict flash flooding with sufficient time for communities to respond in understudied and infrequently monitored headwater systems; and what current and future flash flood risks look like in stream-adjacent communities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

A research program called GREENBURST led by scientists at West Virginia University (WVU) will use radio observations at the Green Bank Telescope (GBT) to study transient astrophysical objects within and beyond the Milky Way. The populations probed by this proposal are: (i) fast radio bursts (FRBs) at cosmological distances; and (ii) ultra-long period pulsars in the Galaxy. Using well-established techniques to search for single pulses and artificial intelligence to combat rising levels of terrestrial radio interference, this project will expand the capabilities of GREENBURST to allow it to sample pulses with widths between 10 nanoseconds and 10 seconds. Two graduate students will be supported by this work and develop highly transferrable skills in data analysis, machine learning, and digital signal processing. In addition, several focused outreach activities will benefit from this work. A K–12 outreach talk describing the science behind the project will be developed by the PIs and students as a new feature presentation in the West Virginia Science Public Outreach Team (SPOT) program. A group of 16 undergraduate student ambassadors will receive training on the presentation and science communication from SPOT and will reach around 32 schools each year. In addition, a short planetarium feature on GREENBURST will be developed at WVU, expected to reach 10,000 members of the local community each year. GREENBURST will be used to carry out four specific projects over a three-year period: two survey projects and two source characterization projects. The first survey is a census of FRBs in nearby galaxies with the GBT. This project uses dedicated time on the telescope and has sufficient sensitivity to target galaxies in which dozens of pulses from FRBs are expected over the duration of the survey. The second survey is a commensal project that searches for ultra-long period (much greater than one second) Galactic sources whenever the GBT is in use. Galactic pulsar-like sources with periods as long as several hours will also be probed through the detection of single pulses. One of the characterization projects targets known repeating FRBs with the goal of finding more examples of so-called “ultrafast” FRB pulses which have recently been shown to exist. The final project synthesizes all the known pulsar detections from GREENBURST to provide a comprehensive census of single-pulse emission which will allow strong constraints on the distribution of giant pulses from Galactic millisecond pulsars, an area of research that is currently poorly understood. 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

For hundreds of years, visible-light waves have been used to detect signals from outer space. Some dark, massive objects in space might not directly emit light waves; a well-known example of this is black holes. Only in the last decade have astronomers begun to be able to directly detect black holes using signals called gravitational waves. The largest black hole binaries--supermassive black hole binaries--are unique in that they are formed in galaxy mergers. This means that they will often exist in rich, messy environments full of plasma, gas, dust, stars, and other materials. This material, when it interacts with a binary system, should light up like a luminous beacon across radio, optical, X-ray light, and more. The black holes in the center of these systems may also emit gravitational waves. A broad suite of instruments (spanning gravitational waves, electromagnetic light and neutrino observatories) are all poised to completely revolutionize our understanding of the full lifecycle of supermassive black hole binaries. Hundreds of published candidates for these objects now exist, however none have yet been conclusively evidenced to be a binary. In this program, the research team aims to organize and publicly serve disparate information about these binary candidates to facilitate massive black hole science in this era of multi-messenger discovery. They will also use the black hole database as the keystone for educational programs from high school through grad school. Existing efforts in West Virginia, such as the Governors’ STEM schools, will facilitate the BI activities. The PIs are developing a public resource to organize information on supermassive binary black hole systems. This "Black Holes Orbiting Black Holes Catalog," BOBcat, will help scientists organize the complex and constantly growing knowledge about the most massive binary black holes in the universe. The information overload from published models will be structured into an online resource that will continue to evolve as further studies are produced. BOBcat will serve as an organizing resource for studies that probe binary intermediate/supermassive black holes with electromagnetic studies, and test massive black hole binary emission theories. This program will establish an already-needed resource that will be a prime facilitator in the multi-messenger era. 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

Flooding is rapidly becoming one of the most widely experienced, deadliest, and costly natural disasters threatening our economy, well-being, and security. While considerable effort has gone into improving flood forecasting models and mapping flood inundation hazards, mountainous settings pose unique challenges. Conditions that generate floods in mountain settings can be difficult to predict and model. Flood hazards in mountain settings are often characterized by erosional hazards that cascade through steep terrain and narrow stream and river corridors, with significant impacts on property, infrastructure, lives, and riverine ecosystems. To develop and employ actionable solutions to address the threat of mountain flooding, a deeper understanding is needed regarding the limits of existing flood forecasting services in complex mountain terrain, the needs of local communities experiencing catastrophic flooding, and the opportunities that nature-based solutions (NBS) afford for improving flood resiliency. Nature-based solutions (NBS) offer low-cost and strategic pathways to flood resilience by employing the services provided by intact forests, floodplains, wetlands, and river corridors as an alternative to engineered solutions to flood mitigation. This planning grant brings together Earth systems scientists, conservation organizations, government officials and planners, and other academic partners to consider the flood resiliency needs of communities, drawing upon examples in the Appalachian Mountains. The project objectives are to (1) assess community-based needs for improved flood hazard prediction, (2) explore the potential of new data sets and data driven modeling approaches to improve flood risk mapping, and (3) develop a pathway for the acceleration of science-based and community-engaged resiliency solutions. The objectives will be achieved through a series of knowledge-sharing webinars, field visits, participatory mapping exercises, and a grant-writing workshop. The overarching goal is to develop capacity for the integration of flood risk prediction science and NBS deployment that is responsive to community needs and builds resilience for highly vulnerable, rural communities in mountain regions. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-08

This EArly-concept Grants for Exploratory Research (EAGER) award supports research to explore novel system architecture designs and agent-level rules to discover beneficial emergent behaviors of multicellular robots, leading to a more natural, adaptive, and resilient robotic system. This project aims to answer three research questions: 1) What conditions foster the spontaneous emergence of complex behaviors in a multicellular robot? 2) How can these emergent behaviors be used for practical purposes? 3) Do self-organized solutions offer greater adaptability and resilience than programmed behaviors in novel situations? Physical experiments will be performed to discover emergent behaviors, gain insights, and evaluate the platform's adaptability and resilience, comparing spontaneous behaviors with a centralized control approach in unforeseen environments. As an early-stage investigation, it aims to enhance the cognitive ability of multicellular robots. The widespread use of robots in everyday environments is limited by their fragility when confronted with unexpected situations. This project aims to create multicellular robotic organisms with problem-solving abilities that emerge from the interactions of simple, identical, and interconnected components operating in a decentralized manner. A ring of robotic "cells" will serve as a primitive multicellular testbed. These cells will attempt to mark the shape and boundary of a "contaminated area" using their bodies without direct programming. Instead, this capability will emerge spontaneously through local interactions among the cells under human guidance. The robotic platform will also be used as an educational tool to promote research, STEAM education, and outreach activities in the Appalachian region. 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

With the increasing availability of high-resolution data, curve-type data have emerged in various fields. Examples include daily precipitation curves, daily pollution level patterns, and intraday stock return curves. As highly fluctuating patterns in these curves become increasingly common due to the growing impact of extreme events, such as unusual weather or financial downturns, it is crucial to effectively analyze and predict these extreme patterns for risk management across all domains. Currently, there is a notable lack of appropriate statistical tools for analyzing extremal behavior in such data, primarily due to its complexity. To address this critical gap, this project aims to develop innovative methodologies for accurate modeling and quantifying extremal patterns in curve-type data. The outcomes of the project have a potential to enhance preparedness for natural disasters and to advances risk assessment in the financial sector. For instance, the tools developed can forecast the likelihood of simultaneous extreme precipitation patterns in different locations, thereby aiding in developing more efficient risk mitigation strategies for natural disasters like flash floods. This capability is crucial in regions with diverse topographies, such as West Virginia, where mountainous terrain with numerous creeks and rivers is susceptible to flash floods during intense rainfall, as seen in the rare 2016 event that caused significant damage and loss of life. The risk assessment tools are adaptable beyond West Virginia, benefiting other states facing similar challenges in managing extreme weather events. Additionally, these tools can help financial institutions manage risk exposure by determining the likelihood of concurrent extreme losses in intraday return patterns across different sectors. Given that millions of Americans have savings in retirement plans, accurately quantifying the risk of catastrophic financial losses is essential. By providing precise measurements of risks associated with extreme market conditions, this project supports national efforts to safeguard economic security. Furthermore, it will contribute to workforce development by training undergraduate and graduate students in statistics and mathematics research. This project introduces a new framework for analyzing and modeling extremal behavior in functional data. The research agenda aims to develop statistical tools for quantifying extremal dependence in paired functional samples and to create statistical hypothesis tests for the independence of heavy-tailed functional time series. Specifically, this project will develop a novel tool—the extremal correlation coefficient—to measure how likely extreme curves exhibit similar patterns simultaneously. For instance, it can answer questions such as: how likely is it for location A to experience heavy precipitation patterns similar to those observed in location B on the same day? Or, during a stock market crisis, do returns of different sectors exhibit similar extreme daily trajectories? Additionally, based on the extremal correlation coefficient, the project will propose a new autocorrelation function and a portmanteau white noise test tailored for heavy-tailed functional time series. Currently, no method exists to detect serial dependence structures in such functional time series. These tools will evaluate the serial correlation of heavy-tailed functional time series and validate the independence of the model residuals. By leveraging the mathematical theory of regularly varying measures for functional objects, the project aims to ensure the asymptotic properties of the proposed estimator for the extremal correlation coefficient and establish theoretical results for the portmanteau white noise test. 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 42nd Southeastern-Atlantic Regional Conference on Differential Equations (SEARCDE) will be held at West Virginia University, Morgantown, WV, on November 9-10, 2024. SEARCDE has devoted itself to bringing together the region’s leading experts and young researchers for over forty years. The meeting provides a valuable venue for researchers to exchange ideas and foster collaborations on questions arising from differential equations and applications. Moreover, it provides an excellent mentorship platform for students, postdoctoral researchers, and junior faculty. It has become a tradition for SEARCDE to include a significant proportion of talks by advanced undergraduates, graduate students, and postdocs, who are encouraged to present their research. Additionally, application-oriented talks that focus on numerical or experimental aspects will interest engineers, software developers, and other professionals in the region. The 42nd SEARCDE is committed to inclusion and diversity. This year, the conference will be held "in cooperation with the Association for Women in Mathematics (AWM)" to boost visibility and awareness and further encourage the participation of women and underrepresented groups. The conference, which consists of four plenary speakers and parallel sessions with invited and contributed presentations, will focus on the following themes: (1) Analytical approaches to solving differential equations with physical, life, and material sciences applications, (2) Numerical and computational methods in solving differential equations, (3) Optimization and applications in control and data sciences. More information about the conference can be found at https://mathanddata.wvu.edu/searcde-2024. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.