Texas Tech University

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY Magnetocardiography (MCG) measures the weak magnetic fields (1 - 100 picotesla, 0.1 - 100 Hz) generated by the heart’s electrical activities using superconducting quantum interference devices (SQUIDs). It complements electrocardiography (ECG) by providing greater detail, as MCGs are more sensitive to currents tangential to the chest surface and can detect vortex currents. Due to its high independence from inhomogeneities in electrical resistivity inside the torso and on the skin, MCG offers a practical alternative for monitoring various cardiac conditions. However, SQUIDs require cryogenic cooling, specialized magnetic shielding rooms, and regular maintenance, making them expensive and logistically challenging to operate. Additionally, their bulkiness and complexity limit portability and require specialized training, further restricting widespread use. This SuRE project aims to advance MCG technology by combining additive-manufactured organic granular magnetoresistive (OgMR) sensors with machine learning for superior cardiac diagnostics. Unlike traditional magnetoresistive (MR) sensors such as giant magnetoresistive (GMR), magnetic tunnel junction (MTJ), and anisotropic magnetoresistive (AMR) sensors, OgMR sensors incorporate magnetic granules (typically nanoparticles) into organic semiconductors, eliminating the need for thin film deposition and micro/nano-fabrication facilities, thus significantly reducing fabrication costs. Additionally, OgMR sensors can be directly printed on flexible substrates with consistent quality in high volumes. Our preliminary results indicate that OgMR sensors offer 50 times higher MR compared to GMR sensors and comparable performance to MTJs, making them a promising candidate for inexpensive, flexible, high-sensitivity magnetic sensors for cardiac signal recording. Given the advantages of OgMR sensors and the diverse expertise of the PI, Co-I, and consultant, we aim to achieve the following specific goals: (1) additive manufacturing and characterizing flexible OgMR sensors; (2) flexible OgMR sensor packaging and external circuit design; (3) flexible OgMR sensors for cardiac rhythm recording and machine learning for cardiac arrhythmias classification. We will develop a palm-sized circuit board with a 5 mm × 8 mm flexible OgMR sensor for ex-vivo and in-vivo mouse cardiac rhythm recording. By the end of this project, we expect to develop a portable OgMR sensing platform for MCG recording and a machine- learning algorithm for the high-accuracy classification of normal and arrhythmic heart rhythms. In the long run, our work aims to revolutionize the MCG technique and reduce healthcare costs.

NIH Research Projects · FY 2025 · 2025-09

SUMMARY Blood cell fractionation using magnetic fields is a promising technology capable of achieving the separation and analysis of blood cells simultaneously, which is essential in clinical diagnosis and can provide insight into important cellular processes that might not be able to be observed easily with other techniques. For example, our previous studies on the analysis of red blood cells (RBCs) using single-cell magnetometry have reported that specific fractions of cells have a low magnetic susceptibility and hemoglobin (Hb) concentration, which is related to the maturity of the cells in circulation as well as a predictor of ex vivo cell aging. Label-free magnetophoresis has also been able to determine that, during the approved 6-week storage period of RBC units in blood banks, the cells lose a significant amount of magnetic susceptibility and iron concentration. Thus, label-free magnetic fractionation can be exploited to separate the healthy, Hb-enriched cells from the damaged and iron-deficient cells in RBC units, in order to extend their shelf life, and to ensure a better utilization of the limited resources we currently have. Also, magnetic fractionation could be used to design novel transfusion therapies that can alleviate the negative effects that patients requiring repeated RBC exchange transfusions suffer, since this technique can be employed to discard detrimental RBCs with low/abnormal Hb from the patient and to recycle the healthy, endogenous RBCs back to the individual. However, devices able to perform the specific separation of large volumes of RBCs into different subsets with homogenous Hb in a short period of time, at a low cost, with high purity, are not available yet. Current devices need to increase the magnetic fields and field gradient distributions in order to perform such separation. In this regard, magnetic responsive materials that can be printed using novel additive manufacturing techniques are a promising option to develop fractionators that can precisely isolate RBC subsets with equal Hb concentration each. We have demonstrated for the first time the excellent magnetic properties that magnetic responsive polymers have for cell fractionation. In this project, we will use a hybrid additive manufacturing approach to fabricate magnetic fractionators with custom magnetic field distributions based on novel magnetic inks and biocompatible microfluidic channels for a high-throughput RBC isolation into 4 subsets based on their Hb concentration. More specifically, our novel approach combines a stereolithography module for microchannel printing and a direct ink writing module for the fabrication of the magnetic field sources by varying the composition of the inks while printing. Various characterization techniques will be employed to assess the quality of the system before we use it for human RBC fractionation. Finally, this work will also strengthen our infrastructure to support undergraduate and graduate researchers and the enhancement of our biomedical engineering research environment at Texas Tech University. 1

NSF Awards · FY 2025 · 2025-08

Many ecosystems change seasonally. Important outcomes may occur during a particular season, such as pests eating crops, but those outcomes are driven by recurring effects between seasons. For example, pests eating during summer affects their hibernation during winter, and survival during winter affects reproduction for the next summer. Accurately quantifying this type of effect is important for understanding long-term outcomes in seasonal ecosystems. This work studies a mathematical description of recurring change, which combines two well-established frameworks into a more complex approach. In particular, this work is concerned with how well the approach can match real-world data. An important factor is whether information obtained from data is accurate and how this affects confidence in mathematical results. The work begins with a simulated test of these problems, before incorporating real data from two studies. The studies concern mosquitoes and bees, highlighting the broad potential applications of this approach. Mosquitoes are common disease vectors that impact human health, and bees provide pollination that impacts agriculture and food security. The project also includes an education component that aims to improve quantitative preparation of biology students alongside interdisciplinary preparation of mathematics students. In this work, feedback between seasonal behaviors is described by hybrid-timescale models. These models couple continuous differential equations (for fast, short-term interactions) with discrete difference equations (for slow, seasonal behaviors). Use of these models may be complicated by intractable analysis and high sensitivity to model inputs. Moreover, their application-level utility depends on the ability to accurately represent true ecosystems. This work will assess parameter estimation and identifiability for these models (or, whether model parameters can be uniquely and reliably recovered from data). Preliminary work will establish appropriate methods and criteria to assess identifiability, using simple models fit to synthetic data. The resulting methods will be applied to empirical data in two applications, under increasingly complex conditions. Taken together, these assessments will establish conditions under which hybrid-timescale models can be practically implemented in seasonal ecosystems. Additionally, the primary educational aim of this work is to revise lower-level math sequences for biology majors. The courses will use a project-based curriculum which builds towards a research experience in mathematics. This project is jointly funded by the Mathematical Biology Program in the Division of Mathematical Sciences and the Population and Ecology Cluster in the Division of Evolutionary Biology in the Directorate for Biological Sciences. 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 · 2025-08

SUMMARY Sickle cell disease (SCD) is the most prevalent genetic blood disorder, caused by a single point mutation in the β-globin subunit of hemoglobin (Hb), the protein contained in red blood cells (RBCs) responsible for oxygen delivery to the tissues. The condition affects millions of people worldwide, primarily of African ancestry or Blacks, who often see themselves treated unfairly when seeking medical care due to the color of their skin. The major complication of the disease is the development of acute pain episodes known as vaso-occlusive crisis (VOC), that result in severe end-organ damage and a shortened lifespan. VOC is caused by vascular occlusion due to the accumulation in the blood of sickle-shaped, rigid, RBCs that become trapped in capillary blood vessels. Since VOC is responsible for an estimated 95% of SCD hospitalizations, and it is also employed as a key predictor of death, it is critical to find a way to drive early detection of VOC to support preventative interventions, better manage the condition and improve the life quality and expectancy of SCD patients. It has been recently demonstrated the protecting role of patrolling monocytes (PMo) in SCD, as they scavenge endothelial-adherent sickle RBCs. We hypothesize that both irreversibly deoxygenated sickle RBCs and PMos count, their magnetic susceptibility, and their intracellular analysis (in terms of iron (Fe), Hb, and RBC content) could be used to predict the potential development of VOC. Due to the magnetic properties of (deoxygenated) Hb, and by extension, irreversibly deoxygenated sickle RBCs, magnetism could be exploited for the simultaneous count and analysis of both cell types (RBCs and monocytes) in SCD and to relate these parameters to the development of VOC. Our recent research has demonstrated a difference between the magnetic/physical properties of sickle RBCs and healthy RBCs, which becomes more evident when the SCD patients are in crisis. We have also demonstrated for the first time the paramagnetic properties of a subset of monocytes, and the higher magnetic susceptibility of PMos in comparison to classical monocytes. Thus, this project aims to study the differences between the number, magnetic properties, size/density, and RBC/Hb/Fe content of RBCs and monocytes obtained from healthy blood donors and patients with SCD at 5 different clinical states (from least severe to VOC), and will try to establish a relationship between these cells’ properties and the development of VOC. We will employ our custom, permanent magnet-based devices for the analysis of the cells and their separation, using fast, inexpensive, and label-free approaches. We expect that single-cell magnetometry can be employed to gain a better understanding of the factors leading to the development and potential prediction of VOC, to determine the severity of the disease and to be used as a quantitative measure for the diagnosis of VOC. Our studies will also provide guidelines for the design of a novel magnetic device for the separation and quantification of magnetic cells in the blood of SCD individuals with potential diagnostic value.

NSF Awards · FY 2025 · 2025-07

The number of Internet-of-Things (IoT) devices has been increasing exponentially. By connecting billions of IoT devices, they will be able to support diverse applications, such as healthcare, energy management, education, social interaction, and public safety. The advancement of these applications, however, hinges on the development of efficient and secure communication techniques to facilitate interactions with heterogeneous IoT devices. This project aims to introduce analysis, models, and techniques to foster seamless collaboration among heterogeneous IoT devices while ensuring their secure communication. The project should enrich the scientific knowledge of communication and security theory, signal processing, and networking. Furthermore, it has the potential to address the grand challenges in crowded wireless channels to support ubiquitous connectivity in heterogeneous IoT networks. Additionally, this project will contribute to the cross-disciplinary development of students at Saint Louis University and the establishment of graduate-level courses on IoT and communication theory. The objective of this project is to exploit, model, and leverage hidden features in heterogeneous IoT devices for cross-layer design, thereby improving the performance of IoT networks and enabling secure asymmetric communication across heterogeneous IoT devices with different communication protocols. The project is structured around two main thrusts. The first thrust introduces models and optimization techniques aimed at improving network performance across heterogeneous IoT devices by reducing latency and overhead, as well as enhancing throughput and reliability. The second thrust focuses on exploring and exploiting the unique and hidden physical-layer features of heterogeneous IoT devices, including spatial distribution of signal distortion and errors, to enable secure cross-IoT communication. 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 2026 · 2025-06

PROJECT SUMMARY Multi-tracer medical imaging improves diagnostics by assessing multiple biological processes simultaneously, providing a more comprehensive view of diseases like cancer, neurological disorders, and cardiac conditions. While current magnetic particle imaging (MPI) offers excellent contrast and sensitivity, it is limited to single-tracer imaging, which can only assess one physiological process at a time. Multi-tracer MPI (MMPI) faces technical challenges due to the spectral overlapping of different tracers’ harmonics in the frequency domain (for system matrix-based image reconstruction method) and the indistinguishable point spread functions (PSF) of different tracers (for x-space image convolution method). The goal of our proposal is to achieve MMPI by using customized tracers and a spectral-separation algorithm for multi-channel, system matrix-based image reconstruction. The PI has demonstrated both theoretically and experimentally a colorization algorithm that separates spectral signals from different tracers using a magnetic particle spectroscopy (MPS) system. MPS is a 0D MPI system without selection or focus fields, creating a field- free region (FFR) to characterize tracers. With successful spectral-separation, tracer harmonics can be divided into separate channels, allowing the Kaczmarz algorithm to be applied for image reconstruction in each channel. Using tracers with distinct magnetic properties, reflected in the differing shapes/slopes of their magnetic hysteresis and Q-factors in the PSF, enhances spectral-separation accuracy and reduces channel leakage. Most magnetic nanoparticle (MNP) tracers on the market have iron oxide cores, leading to similar magnetic properties that cause significant channel leakage, and poor spectral-separation, thus, worsening MMPI performance. To address this, we will design and customize cost-effective, rare-earth element-free, biocompatible spinel ferrite MNP tracers with high magnetic moments. By doping divalent metals like Zn2+, Mn2+, and Mg2+ into Fe3O4, we can alter the cation distribution and overall magnetic moments. The tunable magnetic properties of spinel ferrites allow the creation of tracers with distinct magnetic hysteresis and PSF, which are expected to solve the MMPI challenges for both system matrix-based image reconstruction and x-space image convolution methods. Additionally, we will coat the tracers with red blood cell (RBC) membranes to further enhance biocompatibility and prolong blood circulation time for potential in vivo applications. For a preliminary demonstration of MMPI performance, we will 3D print magnetic phantoms containing multiple tracers (N = 1, 2, 3, and 4) with varying tracer amounts, line widths, and gaps along the x, y, and z axes. These phantoms are designed to assess spatial resolution in resolving multiple tracers in a single scan. The MMPI demonstration will take place at our collaborator’s lab at NIST. A key advantage of this MMPI approach is its compatibility with the existing MPI platform concerning signal collection and for each separated channel, the traditional Kaczmarz algorithm can still be used for image reconstruction.

NSF Awards · FY 2025 · 2025-06

The objective of this EArly-concept Grants for Exploratory Research (EAGER) project is to advance the field of magnetic soft robotics by demonstrating a proof-of-concept mechanism for independently actuating multiple robots with a single external magnetic field. The underlying principle is essentially a magnetically controlled "combination lock," that prevents the robot from deforming in response to a command unless the command is preceded by the correct lock combination. The soft robots under study have potential applications to biomedical devices, surgical tools, and manufacturing. This effort is expected to advance scientific understanding of magnetic interactions in magnetic materials and soft structures, advance the use of machine learning for soft robot design, and promote progress in the field of small-scale soft magnet robots. The goal of this project is to design, fabricate, and evaluate a novel modular magnetic actuation mechanism that operates at scales ranging from millimeters to centimeters. This mechanism integrates an external dynamic magnetic field with innovative structural designs for soft robots to achieve decoupled, multidirectional tuning within individual units, and selective actuation across large arrays of robots. Design methods based on machine learning will be used to guide the design process and ensure that each robot is actuated reliably and independently. Additively manufactured and microfabricated prototypes will be systematically characterized to evaluate key performance metrics, including response time, actuation force, and durability. By addressing the longstanding challenge of achieving independent actuation under a common magnetic stimulus, the project is expected to provide fundamental insights and establish new design principles for scalable, reconfigurable soft robotic systems capable of executing complex coordinated tasks. 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 · 2025-05

This grant will support a community conference in 2025 to review and advance a Lightning Modeling Grand Challenge Roadmap. The three-day workshop will include a day of invited talks on components of the grand challenge, followed by a series of breakout discussions to engage the community in refining the roadmap. About 150 attendees are expected. The conference aims to address the grand challenge of integrating existing knowledge to end-to-end models for either forecast operation or scientific understanding. The conference has three objectives: 1) provide a venue to foster community discussion of the roadmap; 2) coordinate the efforts of the lightning physics and meteorology community to review scientific hurdles to progress and discuss solutions, updating the roadmap document; 3) work together in a focused way on pragmatic implementation plans and identification of resources needed to engineer a concrete, interconnected lightning model. The conference will include a broad cross-section of the lightning physics and lightning meteorology communities, including students and interested participants from university, federal laboratory, industry. Integrative coordination of the lightning community will mitigate the limitations of siloed progress by smaller groups, as is required to achieve the vision of the Grand Challenge. Development of the lightning model will not only address fundamental science challenges, but will foster new applications of lightning science, new sensor development, and support other agency missions across the nation and 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 2025 · 2025-05

Numerous windstorms, including hurricanes, tornadoes, and thunderstorms, strike the United States every year, inflicting extensive damage to buildings and other infrastructure such as structures in power, transportation, communication and information technology systems. This causes devastating life and property losses as well as prolonged interruption to critical societal functions. Even worse, future losses from windstorms are likely to increase because of the aging of buildings and other infrastructure as well as the growing population in hazard-prone regions. The Industry-University Cooperative Research Center (I/UCRC) for Wind Hazard and Infrastructure Performance (WHIP Center) is a consortium of major stakeholders for wind hazard resilience, which include academic institutions, government agencies and industry members from multiple sectors such as insurance, risk management structural engineering and renewable energy. By performing research of interests to these stakeholders, it addresses the multifaceted challenges presented by windstorms and develops solutions that can be expeditiously transferred to industry applications and generate societal impacts by enhancing the economic competitiveness of the U.S. and the well-being of its citizens. The WHIP Center adopts a broad-based approach aimed at serving its members and, through that, society. Its major research themes are characterization of wind hazards, assessment of performance and vulnerability of buildings and other infrastructure, improvement of community resilience, and reduction to societal impact by wind hazards. Within these themes, the faculty and students in the Center use analytical, experimental and numerical approaches to generate actionable solutions based on the needs of the industry members. The Texas Tech University (TTU) Site has a world-renowned wind-related research and education program with more than 50 years of history. The program administers a suite of world-class research facilities that include one of the largest tornado simulators in the world, a boundary-layer wind tunnel, unique Ka-band research grade mobile radars for measurements of windstorms, a 200 m meteorological tower instrumented at 10 heights for high fidelity measurements of wind and other meteorological conditions, and a debris impact facility. These research facilities are utilized by faculty and students from multiple disciplines that include engineering, atmospheric science and socioeconomics to pursue research sponsored by the WHIP Center and generate the envisioned societal impacts. As the Lead Site of the Center, Texas University will manage the operation of the Center that also includes a Partner Site at Florida International University and an Affiliate at Florida Institute of Technology. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-04

The thickness of the Earth’s crust, from the surface to the mantle, varies by location. Geophysical tools can tell us about today's crustal thickness. However, they cannot determine the crustal thickness in the geologic past or how it has changed over time. Knowing the crustal thickness above ancient subduction zones helps us understand several processes, including the formation of mountains, ore deposits, sediments, and the continental crust, where people live. Researchers commonly use compositional markers of rocks to estimate crustal thickness. However, these compositions may be averages, resulting from many processes that rocks experience during their formation. As a result, there are large uncertainties in determining paleo crustal thickness. The research team will study the melts that formed the final rock composition, using minerals that crystallized early in the magma's history. The calculated melt compositions will be used to test whether the magmas formed in thick or thin crust, and how this changed over space and time. The research team will collect data from four ancient arc segments in the western US and will create field research videos to showcase their findings and raise awareness about geoscience research. Collaboration between institutions will create new pathways in field and laboratory science for 10 students engaged in the research project. Petrologic proxies of arc crustal thickness are often applied to plutonic datasets in ancient, exhumed arc systems to generate an estimate of paleo-arc thickness. However, studies have recognized that bulk-rock trace element ratios in plutonic rocks are composite signals of pressure-dependent crystallization and subsequent magmatic processing. Crystal accumulation is of particular concern, since bulk-rock trace element ratios in samples with accumulated crystals do not represent melt values and thus may not retain information about the source depths from which melts were derived. The research aims to quantify the extent of this processing and to investigate the underlying petrologic mechanisms that define the composition-crustal thickness relationship, utilizing mineral-melt equilibrium relationships in high-temperature minerals. The research team will use high temperature minerals to 1) identify and quantify crystal accumulation in plutonic samples; 2) reconstruct trace element melt compositions and evaluate the preservation of pressure-dependent signals; 3) assess melt evolution across four arc segments that preserve different instances of arc crustal thickness: across space, through time, and vertically through the crustal column. Investigation of the underlying petrologic processes that define compositional-crustal thickness relationships is critical to our understanding of the effects of melt fractionation and crystal accumulation on trace element signatures in rocks and minerals. Findings will enable testing of existing models, and potentially generate new models, for magma processing and connectivity in the arc crustal column, and contributes to the larger, first-order question of where most magmatic differentiation processes occur in the crust. The research team will create field research videos to showcase their findings and raise awareness about geoscience research. Collaboration between institutions will create new pathways in field and laboratory science for 10 students engaged in the research 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 2025 · 2025-04

In prokaryotes, the expression of clusters of genes, typically functionally related, often is controlled by a single regulatory sequence leading to a single transcript molecule that encodes multiple proteins. In contrast, in eukaryotes, each nuclear gene has its own regulatory sequence, and each gene leads to a separate transcript encoding a single protein. A few organisms in the animal kingdom show exceptions to this paradigm, and transcripts encoding multiple proteins have recently been found in green algae, which are related to land plants. This suggests that the expression of clusters of genes resulting in transcripts encoding multiple proteins might be more common in the plant kingdom than previously thought. This project uses DNA and RNA sequencing, bioinformatics approaches, and a combination of in vitro and in vitro experiments to explore the presence and functional significance of transcripts encoding multiple proteins among species in the plant kingdom. Plants are the primary producers on our planet, and a better understanding of the mechanisms they employ for gene expression, may enable new approaches for plant breeding. The research program will be integrated with educational and outreach activities designed to engage learners of all ages and increase exposure to STEM disciplines. The education-by-research component of this project will help inspire and train the next generation of scientists. Recent studies have provided evidence of polycistronic gene expression in diverse green alga, highlighting the evolutionary conservation of polycistronic mRNAs among Chlorophytes. Polycistronic mRNAs in land plants have often been regarded as the result of accidental read-through, and little research has studied the prevalence of polycistronic gene expression across Archaeplastida. This CAREER project aims to determine how widespread polycistronic expression is in Archaeplastida and investigate its functional and evolutionary significance. Research activities will (1) generate a map of the polycistron landscape of key Archaeplastida species using long-read sequencing platforms for whole genome sequencing and poly(A)-tailed RNA sequencing, along with database mining and bioinformatic approaches; (2) study the evolutionary dynamics of polycistronic transcripts through ribosome deep sequencing to assess active translation; and (3) investigate the mechanisms of polycistron translation using in vivo and in vitro assays. These experiments will test the hypothesis that polycistrons are evolutionarily conserved at the structural and functional levels within Archaeplastida. The project integrates an education and outreach program, which includes Science Discussion Seminars and training in all aspects of research. It also includes science demonstrations, hands-on activities, and the development of educational materials for K-12 students and the public. A central product of these efforts will be a public database documenting polycistronic sequences across Archaeplastida species. The outcomes of this research are expected to significantly advance our understanding of the evolutionary dynamics of gene organization and expression in Archaeplastida and shed light on the broader implications of polycistronic gene regulation. 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 · 2025-03

Fostering positive science, technology, engineering, and mathematics (STEM) identity and providing access to quality STEM learning opportunities that connect with youths’ local contexts and interests is essential for learning and growth. However, access to quality STEM education experiences can be limited among historically underserved youth. Furthermore, women and individuals identifying as Latinx or Black remain underrepresented in STEM fields. Responding to community needs, this project will develop and study a curriculum that leverages sports to teach STEM content and practices to youth in informal learning settings. While the idea of contextualizing STEM learning through sports is not new, the proposed work is particularly important in that it focuses on youth aged 8 through 11, while most of the existing research on STEM and sports centers on students in middle school and beyond. In close partnership with a historically underserved community, the project team will advance youths’ STEM content knowledge, practices, and identity based on community goals, values, and assets. Guided by sociocultural learning theories, the positive youth development model, and theories of STEM identity development, the project team and participating community members will co-develop a STEM and sports program that is rooted in the local context, community-identified goals, and youths’ own interests. Using participatory design research techniques and building on a pilot program, the project will develop a six-week STEM program, Teaming STEM + Sports, that is aligned with national STEM and physical education standards for youth aged 8 through 11 years. The project team will collaborate closely with youth, parents, community members, educators, and nonprofit organizations throughout the project. Community and nonprofit partners will implement the curriculum each summer in community settings over four years, reaching over 250 youth participants. Using iterative descriptive studies as part of the development process and a quasi-experiment to test the program’s impact, the project will address the following research questions: (1) To what extent do youth perceive the program as culturally relevant, connected with their lived experiences, and engaging? (2) How does participation in Teaming STEM +Sports associate with youths’ STEM content knowledge and STEM identity? (3) What is the effect of the program on youths’ mathematics and science content knowledge? (4) To what extent are youth provided with opportunities to use STEM practices? (5) To what extent do facilitators perceive the program as feasible to implement? Insights related to collaboration and codesign will be transferable to other contexts, and digital versions of the curriculum will be available on a project website, enabling others to adopt the Teaming STEM + Sports program and adapt it to their local contexts. 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 project will fill a gap in informal STEM education resource availability, resulting in open-source STEM and sports learning materials. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2025 · 2025-02

Neurodegenerative diseases (for example, Alzheimer's disease, Parkinson's disease, multiple sclerosis) impact millions of people in the United States and result in hundreds of thousands of deaths. These disorders can affect people of all ages, although they are more common in older adults. Digital twin models, leveraging the exponential growth of biomedical data and artificial intelligence and data science techniques, are opening exciting avenues to obtain new insights into these diseases and revolutionize their treatment and prevention. The investigators will address multiple problems on this interface, and develop data science-driven theoretical foundations, methodological tools and algorithmic principles for several aspects of digital twin models towards better understanding of digital twins as a whole, and in particular in the context of their use in neuroscience and in prevention, treatment and better understanding of neurodegenerative diseases. They will also address the ethical, legal, and social implications of using digital twin models in the context of healthcare in general, and in studying neurodegenerative diseases using magnetic resonance-technology driven images (MRI) in particular. This research will greatly aid in the deployment of digital twins in medical and healthcare practice, and will significantly advance neuroscience and the study of neurodegenerative diseases. The investigators will address open problems in low-dimensional manifold learning, causal pathway searches and feature discoveries and selections, and develop multiple techniques for verification, validation and uncertainty quantification of digital twins using Bayesian techniques, data assimilation, resampling, empirical likelihood methods and topological data analysis. They will also develop dynamical system models, incorporating observational image data, for computational efficiency and synthetic data generation for ethical use of artificial intelligence and digital twin technology in studying neurodegenerative diseases. Additionally, they will develop knowledge graph driven systems for use by regulatory and other healthcare monitoring agencies for de-risking and easy implementation of data-driven modern technologies. The investigators will work in conjunction with regulatory and other healthcare governing agencies towards better understanding of neurodegenerative diseases and successful deployment of data-driven technologies to mitigate suffering from such diseases. The investigators will mentor, train and teach students on various aspects of digital twins, data science and neuroscience and their interconnections, and will help build a highly skilled workforce on these topics. 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 Faculty Early Career Development (CAREER) grant will advance scientific knowledge of the flow physics of rooftop vortices from hurricane-type strong winds on low-rise buildings and develop bio-inspired flow control strategies to attenuate the damaging effects of roof suctions on building resilience. Dramatic damage repeatedly occurs on low-rise building roofs during windstorms, as observed in recent hurricanes Matthew (2016), Maria (2017), and Michael (2018). Building roof failure often starts at the windward roof edges and corners, where extreme peak suctions are induced by flow separation and unsteady vortices. Improved understanding of vortex dynamics governing the worst roof suction and smart flow control strategies by learning from nature will contribute towards more accurate wind load prediction, enhanced wind design provisions, and reduction of wind-induced damage and economic and life losses, and thus advance post-windstorm national welfare and prosperity. The bio-inspiration approach will not only produce cost-effective, high-performance mitigation strategies for low-rise buildings, but will also motivate new thinking in broader engineering fields. This research will use the Natural Hazards Engineering Research Infrastructure (NHERI) Wall of Wind (WOW) facility at Florida International University (FIU). Experimental datasets will be archived in the NHERI Data Depot (https://www.DesignSafe-ci.org) and be made publicly available for validation of computational fluid dynamics (CFD) models. To strengthen the persistence of engineering students, first-year undergraduate students will be engaged in a new learning community by integrating scientific questions into team-based, early research experiences, as well as weekly open workshops and invited seminars. The learning community program will improve the STEM infrastructure, broaden underrepresented groups’ participation in engineering, and build a pipeline for the engineering workforce. This project will support the investigator's long-term career vision focused on fundamental research on wind-structure interaction and bio-inspired flow control to increase the wind resilience of civil infrastructure that contributes to community resilience and sustainability. This award contributes to the National Science Foundation's role in the National Windstorm Impact Reduction Program (NWIRP). The specific research objectives are the following: (1) quantify three-dimensional, transient rooftop vortices from hurricane-type high winds of high Reynolds numbers, (2) correlate the unsteady vortices with roof peak pressures, and (3) utilize bio-inspiration as an innovation tool to create cost-effective wind mitigation devices, ultimately enhancing the wind resiliency of low-rise buildings. A series of well-controlled wind tunnel experiments with unsteady flow and pressure over a scaled low-rise building model will be conducted at Cleveland State University and the FIU WOW facility. Systematic measurements of the unsteady three-dimensional vortical flow at high Reynolds numbers will also be beneficial to the broader fluid mechanics community to advance understanding, modeling, and control of a wide class of vortex flow phenomena. The research will result in (1) vortex flow mechanisms governing the peak roof suctions at high Reynolds numbers, and (2) bio-inspired, cost-effective mitigation strategies (porous fractal parapets) to manipulate vortex formation, applicable to new and retrofit of existing low-rise buildings. 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

Advancements in science and engineering increasingly require fast and accurate computational methods for simulating complex multi-physical processes and their interactions occurring at a wide range of temporal scales or high frequencies. Numerical weather prediction and climate modeling are notable examples of such applications that rely on the computational solution of primitive equations, which are used to predict the behavior of the atmosphere, oceans, land surface, ice, etc., as well as the complex interactions among them. Numerical solutions of such multiphysics problems remain a challenging task due to the presence of multiple time scales in the system where different processes take different amounts of time to complete. As such, developing advanced numerical methods capable of offering fast and reliable solutions is crucial for many applications that rely on large-scale simulations of complex systems. The overall goal of this project is to develop novel time integration methods for stiff and highly oscillatory systems and demonstrate their performance on applications such as numerical weather prediction, ocean modeling, and molecular dynamics simulations. Additionally, the project aims to train one doctoral student and offer opportunities to undergraduate and graduate students in mathematics at Mississippi State University. Training of at least one graduate student on the topics of the proposed work is expected. The project has four primary aims. First, the investigator will derive novel mixed exponential integrators and preconditioned rational exponential integrators for stiff systems and implement them. Second, the investigator will develop stiffly-accurate embedded multirate exponential methods for additively partitioned systems. Third, the investigator will develop stiffly-accurate exponential Nyström methods. Fourth, the investigator will investigate the performance of the newly developed methods on applications in numerical weather prediction, ocean modeling, and molecular dynamics simulations. The investigator will build off of his previous expertise in constructing, analyzing, and implementing exponential and multirate time integration methods to achieve these aims. Ongoing collaborations with numerical analysts, meteorologists, and computer scientists will also contribute to the success of the 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 2025 · 2025-01

The objective of this project is to scientifically investigate a framework for user-driven design for product personalization supported by computational agents. This research is carried out in the context of 3D food printing, which enables the fabrication of foods on-demand with customizable ingredients, textures, and shapes. This research will enable the creation of a design framework and tools to aid personalized food design, which can produce appealing soft foods for persons with swallowing difficulty, customized foods with optimized nutrition, and tailored foods to assist in treating eating disorders and/or for otherwise selective eaters. It is difficult to design and deliver desirable 3D-printed foods for users due to each user’s subjective preferences for taste and shape, while also considering the challenge of fabricating foods with complex 3D-printed shapes and suitable texture. The research supposes that a human-computer design approach can satisfy subjective user needs through human design decisions, while computer algorithms can optimize for objective factors of nutrition and food mechanics. Additional project deliverables include enhanced teaching content for engineering design courses, sharing of design tools with researchers and industry, and hands-on demonstrations of design personalization with 3D food printing for students and the general public. The overarching goal of this project is to create and validate a generalizable scientific framework for an innovative user-driven design approach supported by computational agents to generate and fabricate personalized foods that are nutritious and appealing for diverse consumers. The research is carried out with human subject experiments, computational algorithms, and physical validation. The research will provide new understanding of user-driven search behaviors, biases, and results as design space complexity and decision availability are varied. User control and computational agent search support will be varied to determine their influences on personalization outcomes. The research is creative and original by having users interact directly with computational agents that adapt to human decisions, which results in a design framework in health that broadly informs personalized design in additive manufacturing and medicine. Streamlined design approaches will benefit food printing’s adoption in military, health, and consumer sectors by offering improved automation for personalized nutrition, while retaining generalizability to additional domains. Developed design tools will be demonstrated in public events, with an emphasis on providing first-hand design experiences for K-12 students that inspire future engineers. 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 · 2024-12

A digital twin is a virtual model that mirrors and updates in real-time based on data from its physical counterpart. In biomedical and healthcare fields, digital twins, representing virtual models of patients, medical devices, and more, can open up new avenues for developing and evaluating innovative biomedical technologies, particularly enabling virtual clinical trials for evaluating cardiovascular medical devices and advancing regulatory sciences. However, current digital twin technologies lack sufficient computational fidelity and efficiency to effectively support these biomedical and healthcare applications. To resolve these challenges, this project aims to develop advanced computational methods for creating high-fidelity, fast-running digital twins of patient hearts and cardiovascular medical devices. Additionally, the methods will be made publicly available through a software/cyberinfrastructure platform. This will facilitate virtual clinical trials that can evaluate the efficacy and safety of medical devices, as well as improve device designs before initiating real clinical trials in a safe, cost-effective, and precisely controlled manner. In addition to advancing digital twin technologies, the project’s cyberinfrastructure will serve as an educational resource for students, researchers, and industrial engineers to enhance their understanding of advanced digital twin techniques for medical device evaluation. This project will develop novel machine learning (ML)-based image analysis algorithms and physics solvers for performing near-realtime virtual clinical trials with high-fidelity digital twins of patient hearts and cardiovascular medical devices. Patient-specific geometries and tissue mechanical properties will be incorporated into the digital twin construction for near-realtime physics simulations. Consequently, virtual clinical trials can be performed at significantly reduced time and financial costs. This project will deliver (1) novel ML algorithms for accurate digital twin geometry reconstruction from 3D+t medical images, enabling point-to-point mesh correspondence for high-fidelity dynamic motion tracking; (2) a robust and computationally efficient inverse method to identify in vivo material properties from medical images, which is essential for creating material-realistic digital twins; (3) a new ML-based fluid-structure interaction (ML-FSI) solver for biomechanics and hemodynamic analyses, thereby enabling dynamic digital twin simulations throughout a cardiac cycle. While the primary focus will be on digital twins of the left heart and aorta, the computational methods can be generally applied to create digital twins of the entire heart. The computational methods will be demonstrated through concrete examples involving Transcatheter Aortic Valve Replacement (TAVR) and Thoracic Endovascular Aortic Repair (TEVAR) devices. The algorithms and methods developed in this project will be generic and readily applicable to devices for treating various cardiovascular diseases. This project is jointly funded by the Division of Mathematical Sciences, the OAC Cyberinfrastructure for Sustained Scientific Innovation (CSSI) program, and the CBET Engineering of Biomedical Systems 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 project serves the national interest by preparing a qualified engineering workforce with important technical and professional skills for the health-based point-of-care (POC) additive manufacturing (AM) industry. Health-based POC-AM is a non-traditional form of manufacturing referring to the just-in-time creation of anatomical models, surgical instruments, prosthetics, scaffolds, etc., based on medical imaging data and need at the place of patient care. The growth of POC-AM requires the collaboration of medical, engineering, and social science professionals in that engineers must be trained to be socially adept and communicative about additive manufacturing specifically for healthcare applications. Despite the exponential growth in POC-AM market value and scholarly activities, the needed education and training components are underdeveloped, especially for undergraduate students in public engineering schools. This IUSE Engaged Student Learning Level 2 project will bridge this talent gap by creating an undergraduate engineering course that is broadly accessible and will be able to define, cultivate, and assess students' technical and professional skills needed by the booming POC-AM industry. This project features a project-based learning plan to develop students' theoretical and hands-on skills to create a broad range of medical objects from non-patient-specific personal protection equipment and anatomical models to patient-specific prosthetics, tissues, and implants. This project will strongly emphasize the development of students' reflective communication skills, both written and verbal, with colleagues in both engineering and in healthcare. The project will also design a protocol for assessing and developing those communication skills using objective and subjective metrics. Thus, the goal of this project is to remove barriers between POC-AM research and education while interconnecting key concepts in multiple related sub-disciplines through teaching this unique skillset to undergraduate students at two large public universities. The innovative course that focuses on students' technical and communication skills development will train holistic and well-rounded engineering students who can solve complex problems that require a broad integration of technical knowledge and communication skills. The combination of cutting-edge learning about POC-AM and a targeted and efficient communication skills development targeted to the needs of the post-COVID student population makes this project highly effective for undergraduate education. The developed instructional and assessment materials will be publicly available as this project can be a model for other similar upper division engineering courses, especially in an emerging and practical field. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. This project is jointly funded by the Established Program to Stimulate Competitive Research. This project is jointly funded by IUSE 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 · 2024-10

This project aims to serve the national interest by strengthening aerial computing education to meet the national workforce demands for cybersecurity experts. Specifically, this project has the potential to addresses privacy and security concerns associated with drones by developing a drone-centric aerial computing curriculum. Using the flipped project-based learning (FPBL) model, students will engage in out-of-class activities. These activities are strategically designed to develop computer science (CS) knowledge and secure time for in-class project-based learning activities to fly drones. With ethical considerations infused into the curriculum, this project intends to prepare the future CS workforce with ethical awareness regarding responsible technology use. The proposed FPBL model for aerial computing could be adopted and adapted in other science, technology, engineering, and mathematics (STEM) courses to help bring more hands-on, real-world experiences to improve undergraduate STEM education. The project's goal is to provide effective FPBL curricular materials and activities to engage students in the programming of autonomous drones for privacy protection and security enhancement at the individual and societal levels. This project will: (1) develop online modules to help students learn CS concepts, ethics, and laws related to flying drones and simulate their codes iteratively before class, (2) engage students in authentic, hands-on, and augmented projects in class to fly drones using advanced algorithms, such as path planning and randomization, and (3) assess the impact of the curriculum in cultivating students' advanced CS knowledge in aerial computing, programming and implementation skills, and ethical awareness pertinent to drone operations for protecting privacy and enhancing security. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its 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 · 2024-10

An award is made to Texas Tech University to acquire the Pacific Biosciences Revio system, a state-of-the-art long-read sequencer, for research across multiple disciplines. This advanced technology will significantly benefit researchers at Texas Tech University, Texas Tech Health Sciences Center, the USDA Stress Laboratory, and national and international collaborators. The Revio system's high throughput and flexibility will enhance genome structure and function studies, plant-microbe interactions, plant sex chromosome evolution, transposable elements, biodiversity, climate change, and agricultural improvement. This acquisition will also provide invaluable training opportunities for faculty, post-doctoral researchers, and students, promoting educational and professional development. Furthermore, the project will contribute to broader societal benefits, such as environmental protection, improved agricultural practices, and increased scientific literacy through outreach activities. The intellectual merits of this project lie in its potential to advance scientific knowledge in several key areas. By leveraging the capabilities of the Revio system, researchers will explore the complexities of genome structure and function, shedding light on the fundamental processes of life. Studying plant-microbe interactions and the structural evolution of plant sex chromosomes will provide insights into evolutionary biology and genetics. Investigations into transposable elements and biodiversity will enhance our understanding of genetic diversity and adaptation. Additionally, research to improve agricultural products will contribute to food security and sustainable farming practices. This project represents a significant step forward in genomics and bioinformatics, promising to yield valuable discoveries and innovations. 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 advances the knowledge in robot intelligence via research on “viability” of robots. Viability is the ability of a system to autonomously maintain itself or recover functionality after an adverse critical event and is a desired feature of robust intelligence and autonomy. An example of a critical event is a robot landing upside down and being unable to stand up without an external assistance. Other examples are an unmanned underwater vehicle in an unrecoverable state, a robotic arm getting stuck in clutter, or a car with poor braking performance due to a mechanical failure. The principal objective of this project is to develop a framework for the design of viable (self-recoverable) artificial agents. The main challenge is to formulate a metric for viability that can be calculated for various scenarios. This metric needs to be computable from the motor and sensor readings. The first task of this project is to develop an effective metric for agent viability from the essential properties of control systems, along with computationally efficient methods for its calculation. The second task is to demonstrate the applicability of this framework for useful problems in (i) self-recovery of walking robots and (ii) self-maintenance of car brakes. This project not only advances the understanding of robot viability, but also proposes new machine-learning approaches for control and analysis of dynamical systems. To achieve its goals, this project will extend the classical notion of Lyapunov exponents towards ’Agent-Induced Lyapunov Exponents’, AILE, which prioritize states with diverse potentialities, and with a large space of effectively controllable states. AILE allows artificial agents to train autonomously, maintaining themselves or recovering capabilities without the need for problem-specific externally provided reward functions. Computational methods will be developed for the calculation of the AILE metric with known and unknown dynamics, and with partially and fully observable state. This capacity will allow for the broad applicability of the framework. It will be demonstrated in two transformative applications: self-recovery of locomotion agents, and control of chaotic ’stick-slip’ friction in car brakes. This project is not framed along the lines of traditional disciplinary boundaries, but rather bridges between machine learning, dynamical systems, mechanics, and information theory. This project will open doors to new research directions and technology for fully-autonomous agents with an enormous potential for replacing and/or assisting humans in risky situations, such as driving. 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 S-STEM Research Hub will contribute to the national need for a well-educated STEM workforce by researching factors that influence the retention and graduation of high-achieving, low-income students with demonstrated financial need. Building on a collaboration among Mississippi State University, North Dakota State University, Indiana Wesleyan University, University of North Alabama, and Texas Tech University, the project will develop a strategic alliance among Rural Serving Institutions (RSIs) to collaborate and conduct research aimed at increasing rural, low-income college students’ success in STEM majors and participation in STEM careers. In particular, the alliance seeks to build capacity to conduct research about important aspects of belonging that can develop, accommodate, and support the graduation of domestic, rural, low-income STEM students. As a result the project will inform ways that RSIs can support the growth of the STEM workforce in rural communities. Specific project activities include building and managing Rural Serving Institution Network Groups to gather and analyze data and insights from the experiences of rural, low-income students who are participating in the NSF S-STEM program. The Research Hub will also provide capacity-building and technical support for STEM faculty at RSIs to conduct education research about the role of belonging in rural student persistence, graduation, and STEM employment. Research results about interventions that better support rural, low-income student success and contribute to the social and economic well-being of the rural communities they serve will be disseminated to the broader community of Rural Serving Institutions. The overall aim of this project is to address the existing gap in rural STEM higher education research about how to support rural, low-income students, who face specific challenges in enrolling in, persisting in, and completing STEM degrees. Three project goals guide the project's efforts. First, is to conduct research through Rural Serving Institution Network Groups that gather and analyze data and insights resulting from the experiences of rural, low-income students participating in S-STEM projects. Second, is to provide capacity-building, technical support, and strategic alliances for researchers at RSIs, including Summer Institutes and Network Group facilitation, to contribute to the collective understanding of rural, low-income students’ enrollment, persistence, and completion of STEM degree programs. Third, and finally, is to disseminate research to share information about what works and what does not for STEM students who are both rural and low-income. 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, transfer, 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 · 2024-10

This project, titled Building Community-Driven Resilience and Empowerment through Adaptive Manufacturing Technologies (Co-DREAM Tech) aims to address the gap in access to advanced building technologies and the essential knowledge for their effective and responsible use in remote and mid-sized communities across the US. By focusing on community-driven resilience and empowerment through adaptive manufacturing technologies, the project seeks to strengthen housing resilience against perils. Co-DREAM Tech introduces adaptive design and construction strategies informed by community needs, leveraging human-centric and context-aware technologies. This project aims to streamline construction processes and enhance community engagement in disaster prevention and mitigation efforts. The broader importance of this project lies in its potential to create scalable and adaptable solutions that can be replicated across diverse communities, including underserved and unincorporated areas within the states of Texas and New Mexico, fostering a nationwide impact on resilient technology development. The Co-DREAM Tech project establishes a foundation for a replicable model that integrates research, education, and outreach tailored to the distinct needs of its communities of interest. The project comprises interconnected tasks that include explorative mapping, contextual identification, hybrid analysis of emerging design-build technologies, and participatory prototyping to engage communities directly in developing adaptive solutions. This strategic approach navigates the unique challenges presented by regional natural and anthropogenic hazards and creates a scalable blueprint for the responsible design, development, and deployment of collaborative design-build technologies across the US. Pilot projects in Texas and New Mexico focus on mitigating risks from extreme events and developing resilient housing solutions. By integrating advanced and emerging design-build technology with community feedback, the project aims to set a standard for empowering communities through informed and adaptable technological development. Additionally, the project will enrich educational curricula with practical, technology-based solutions, providing specialized training to underrepresented groups and establishing a scalable model for sustainable and ethical technology 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

Hazardous weather early warning systems disseminate timely and meaningful information about flash floods, tornadoes, and other weather hazards so that individuals, communities and organizations can prepare for and protect themselves against harm or loss. Early warning systems involve sensors, model forecasts, federal and local public safety organizations, private companies, and communications technologies that disseminate warnings to the public. Hazardous weather warning messages are general rather than tailored to the risks faced by the people who receive them. The same warning message goes out to everyone in an affected region, regardless of individual circumstances. Each person is responsible for ensuring they receive and understand the warning, figuring out if they or loved ones are at risk, and then deciding if they have the capability or interest in taking protective actions. While warning systems have been effective for segments of the population, there is great potential to improve individual-level decision-making and community/societal outcomes, especially in the face of rapidly intensifying weather events. This planning grant takes a human-centered approach to hazardous weather warning to: 1) develop a deeper understanding of how individuals assess their risk and take action as weather hazards evolve, and 2) apply this expanded knowledge to new ways of tailoring warnings to individual or group circumstances. In this planning grant, a multidisciplinary group of researchers and practitioners address how multiple factors – rain intensity, quality of the stormwater infrastructure, individual daily routines of travel, advanced preparation, risk perception, warnings, social and environmental cues, and socioeconomic vulnerability – interact to influence people’s perception and response to floods. The team establish a common knowledge base and language through sharing research, methods, and datasets. A focus group is held with residents of vulnerable communities in collaboration with a local nonprofit to investigate how different individuals process information from early warning systems. The planning project includes exploratory projects that contribute to an innovative plan for convergent human-centered research. This work identifies new relationships among risk perception, mobility, weather, and built infrastructure that can point to new directions for convergent warning research. In addition, the planning grant allows early work on developing the concept of personalized warnings. Broader impacts include outreach to vulnerable populations to learn about this group’s perceptions and actions during floods. 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 2026 · 2024-09

Abstract: Approximately 3% (500,000) American children are blind or visually impaired – defined as having difficulty seeing even with correction. Children who are blind or visually impaired (CBVI) report lower health related quality of life and experience higher rates of chronic health conditions and mental health disorders compared to children with typical vision. Despite the prevalence of childhood blindness being greater than or equal to other rare, but well-researched, childhood conditions (e.g., childhood leukemia), research is virtually nonexistent regarding psychological, social, and environmental factors that directly (and uniquely) impact the wellbeing, health, and development of CBVI. Thus, there is a critical need to understand how psychosocial and developmental factors contribute to mental and physical health outcomes among CBVI. The objective of this research project is to extend and expand our preliminary findings that highlighted a link between negative psychosocial experiences and poorer physical and mental health of CBVI by: (1) recruiting a first of its kind, large, national sample of CBVI; (2) obtaining detailed measures of external negative social experiences including bullying, peer rejection, and social isolation and the negative affective states and cognitions that result from these experiences from the perspectives of both caregivers and children; (3) using advanced multivariate analytic techniques to identify the contributions of these factors – beyond other relevant contributors to health – to physical and mental health outcomes among CBVI; and (4) obtaining multi-method, multi-informant assessments of physical and mental health status to further validate our findings. By elucidating how the social environment of CBVI contribute to the physical and mental health and wellbeing of these children, the proposed research will foster the development of interventions aimed at improving long-term health outcomes and promoting resilience among CBVI – thereby having an important positive impact on the growth, wellbeing, and development of this population. We will achieve the overall objectives of this proposal by assessing 500 CBVI ages 9-17 and their caregivers from across the United States with respect to both external and internal processes related to their social interactions and experiences and child physical and mental health status. We will collect data on potentially relevant covariates including nature of visual impairment and presence of other disabilities.