Pennsylvania State Univ University Park

NSF Awards · FY 2024 · 2024-12

The broader impact/commercial potential of this I-Corps project is the development of a software for the optimal design and operation of data center cooling system to reduce energy consumption. The proposed solution offers a path towards dramatically reduced energy consumption in data centers, a sector known for its high electricity demands, primarily due to cooling needs. Data centers in the U.S. use about 2% of the nation's electricity, with half of that for cooling. The global data center cooling market is projected to grow from $14.85 billion in 2022 to $30.31 billion by 2030. With its proven ability to achieve up to 70% energy savings, the proposed software addresses a critical need for more efficient and sustainable operations in data centers, which are increasingly vital to the global digital infrastructure. This leap in efficiency not only lowers operational costs but also significantly reduces the carbon footprint of data centers, aligning with global efforts to combat climate change. The widespread applicability ensures that it can contribute to energy savings and sustainability goals across the industry spectrum, from small-scale operations to mega data centers. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of software for data center cooling design and operation using equation-based complex systems modeling language. The system will offer a streamlined workflow with user-friendly interfaces, automated modeling, and optimization processes powered by a comprehensive library of predefined system templates. The proposed technology will ensure automatic adherence to data center energy standards to bridge the gap in energy code compliance. The solution integrates an optimization engine with machine learning to automatically determine the optimal control settings based on outdoor weather conditions and data center IT load, reducing cooling energy consumption while maintaining system reliability. Additionally, its fault detection capabilities identify various equipment and system faults, even non-critical and subtle ones, helping to save energy, extend equipment lifespan, and prevent catastrophic events. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

This award provides funding to support about 10 United States-based students to attend a doctoral consortium (DC) at the ACM 2025 ACM International Conference on Supporting Group Work (Group 2025), to be held in Hilton Head, South Carolina. The Group 2025 DC focuses on participating students' doctoral dissertations, which represent state-of-the-art research in the areas of organizational systems, information systems, social informatics, and computer-supported cooperative work. The DC provides both an opportunity for these projects to be shaped through intellectual exchange as well as an opportunity to communicate the character of the work to a key group of early career researchers. Bringing together students and experienced faculty, both during the DC and during the conference poster presentations, will foster interdisciplinary conversations that are valuable for both the participants and the Group community as a whole. Through a structured program both during the DC and the conference, students will gain a number of benefits in terms of valuable scholarly critique, career advice, and connections to senior researchers. Both participating students and faculty will be a diverse group in may ways, both in terms of their intellectual and demographic backgrounds. This diversity will help broaden participants' horizons at a critical stage in their professional development through providing a wider range of expertise and perspectives on the work. Further, DC participants often later become leaders in the community themselves, both around research and through mentoring future junior scholars, growing and sustaining the community. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Cuscuta (common name: dodder; Convolvulaceae) is a diverse genus of parasitic plants that causes major crop losses across the US and the globe. While dodder that are major pests attack a wide range of host species, many show host preferences, and dodder growth varies substantially across hosts. This apparent host preference suggests some dodder genotypes and populations are adapted to a particular set of hosts. Understanding the genetic mechanisms of these host preferences could help farmers in their battle against dodder. The central biological question of this project is: how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts? The research team will study variation in DNA sequence and gene expression across diverse dodder populations across diverse host species to answer this question. Additionally, the team will pursue agronomic research, extension, and outreach as a part of broader impacts. In particular, the team will identify dodder-resistant cultivars of blueberries and determine the role of over-wintering of dodder in driving subsequent year infestations. Additionally, lessons in plant biology and research projects will be developed with blind and visually impaired and deaf and hard of hearing students. The research team will address the central question, how do agriculturally relevant Cuscuta species successfully parasitize a wide range of hosts, with three main aims: 1) profiling the diversity of trans-species miRNAs across Cuscuta populations and identifying their host mRNA targets, 2) resequencing Cuscuta genomes to characterize population genetic processes affecting miRNA diversity and host-specificity, and 3) determining how genetic variation in Cuscuta response to hosts is driven by gene expression and associated with success of attachments to hosts. The study will cover the two most common species of Cuscuta in the study region of the northeast US, C. campestris and C. gronovii. Small RNA sequencing will be used combined with host transcriptome assemblies to study the diversity of trans-species miRNAs and their targets across the region. Inference of the importance of miRNA variation will be tested using mutants in host mRNA targets. Population genetic inference will be applied to understand what evolutionary processes have shaped diversity in host-responsive genetic loci. Microscopy and RNAseq will be used to study how genetic variation in Cuscuta interacts with different host species to determine attachment success and gene expression. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

Prime editing (PE) is a recently developed, cutting-edge CRISPR/Cas gene editing technology that can accurately replace, add or remove specific pieces of DNA in an organism. Due to its high accuracy and versatility for gene editing, PE offers significant advantages over the conventional CRISPR/Cas technology and is a highly promising tool for basic plant research and applied crop breeding. This project aims to develop highly efficient and robust PE systems in rice, tomato, and poplar plants. The resulting PE tools are expected to facilitate fundamental studies in plant biology and genetic improvement of agricultural crops for important traits such as high yield, superior quality, disease resistance and abiotic stress tolerance. This project will also provide multidisciplinary research training in plant biology, functional genomics and genome engineering to postdoctoral fellows as well as graduate and undergraduate students. Outreach activities will involve local K-12 students, growers, and the general public through already established programs or newly created workshops or activities at Penn State, University of Maryland, and the National Rice Research Institute in India. Prime editing is a revolutionary and advanced CRISPR/Cas genome engineering technology that enables almost all forms of precise gene editing, including base substitutions, insertions and deletions within the genome. This versatility makes PE a highly valuable tool for precise editing of plant genomes, with broad applications ranging from basic research in plant biology (e.g., epitope tagging of endogenous proteins) to practical usage in crop breeding (e.g., creating desirable alleles). Despite its successful applications in the mammalian cells, high efficiency PE in plants has only been demonstrated in rice. To fully realize the potential of PE and meet the urgent need of plant biology and crop breeding communities for precise genome editing, it is imperative to develop highly efficient and robust PE systems in plants. This project aims to optimize prime editor proteins to increase their activity and editing efficiency, improve pegRNA structure and expression for single and multiplex prime editing, and modify plant DNA repair systems to increase PE efficiency. By incorporating these innovative features and extensively testing them in rice, tomato, and poplar, highly efficient next-generation PE systems are expected to be developed for precise genome editing of both monocot and dicot plants. All data and project outcomes including plasmid vectors will be made available to the broader research communities through publications and public repositories. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-11

The international conference "Frontiers in Sub-Riemannian Geometry" will be held at the CIRM, Marseille (France) during the week of November 25-29, 2024. The aim of the conference is to bring together researchers working in different areas of mathematics related to sub-Riemannian geometry, with different backgrounds, to share the most recent results with multiple points of view and so to foster interactions between research groups and to contribute to the training of young researchers. This award will provide support for U.S. based participants. Sub-Riemannian geometry has grown significantly since the early 1990’s. It is closely related to several areas in mathematics such as geometric control theory, the theory of partial differential equations, geometric measure theory, etc. Sub-Riemannian geometry also plays a major role in many mathematical applications such as robotics, quantum control, and neurogeometry. For more details, see the website: https://conferences.cirm-math.fr/3091.html 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

Past and current human populations experienced drastically different environments and ways of life. Consequently, researchers hypothesize that a mismatch between past adaptations and modern environments is contributing to the current rise in non-communicable diseases (e.g., type 2 diabetes, obesity, cardiovascular disease). The concept of evolutionary mismatch suggests that disease-associated genes were beneficial, and thus selected for, in past environments. In modern ones, however, these same genetic variants may lead to negative health consequences. This study examines the evolutionary history of genetic variants associated with type 2 diabetes by: (1) determining if there is evidence of past positive selection, and (2) testing whether genetic variants that were selected in the past interact with current environmental factors affecting type 2 diabetes. The study builds on existing collaborations with local research institutions and physicians. Workshops are offered to the general public and scholars. Educational materials are developed and provided to those affected by type 2 diabetes. Scientist have yet to determine whether, and to what extent, an evolutionary mismatch explains the recent world-wide increase in non-communicable diseases. Using a multipopulational approach, that combines two genome-wide association studies with two evolutionary genomics statistics, this project aims to: (1) comprehensively identify genetic variants associated with type 2 diabetes, (2) test these variants for signatures of past positive selection, and (3) examine whether past positive natural selection affects risk-increasing alleles or protective ones. This gene-by-environment study focuses on a rapidly changing population (i.e., increasingly market-integrated diet and lifestyle). This investigation informs about the portability of the genetic architecture of type 2 diabetes. Examining gene-by-environment interactions reveals the extent to which the genetic effects of type 2 diabetes-associated variants depend on current environmental and lifestyle factors. This project establishes a framework that can be applied to investigations of other mismatch diseases. 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

As the demand for wireless data keeps growing and new technologies like augmented reality (AR), virtual reality (VR), and self-driving cars become more common, the next generation of mobile networks must handle extremely high data speeds—up to Terabits per second—within the next ten years. Ensuring safe autonomous driving requires advanced radar systems with extremely high-resolution imaging capabilities. Military aviation also needs ultra-high data rate (>10 Gbps) airborne connectivity to support complex missions and share information in challenging environments. Developing these capabilities involves creating new wireless communication and sensing devices utilizing large available bandwidth and small wavelengths offered by sub-THz spectrum above 200 GHz. However, the severe path loss must be overcome using large transmitter and receiver arrays. Implementing large transmitter and receiver arrays above 200 GHz using the current semiconductor and packaging technologies presents significant thermal, electromagnetic, and mechanical challenges. To solve these issues, we propose scalable 240-GHz transmitter and receiver arrays based on new application-specific array architectures, innovative silicon-based beamformers, high-performance InP power amplifiers (PAs) and low noise amplifiers (LNAs), and advanced packaging technology. The proposed work is expected to serve as the basis for next-generation wireless connectivity and sensors to drive the integration of digital, physical, and human worlds, fostering growth and innovation across industries. The proposed program will educate the next generation of scientists and engineers through specialized training programs, equipping them with integrated circuit design, packaging, antenna design, and radar and communication systems skills. It will also provide them with synergistic collaboration opportunities to work on the co-design across different areas of engineering. The research outcomes will be utilized to develop short courses and certifications to teach critical manufacturing processes at Penn State University, targeting students from diverse backgrounds and working engineers who need advanced packaging and system integration skills. The proposed research will advance semiconductor and packaging technologies for next-generation wireless communications and sensing through new scalable sub-THz phased arrays using heterogeneous integration. The proposed array architecture shifts from traditional two-dimensional (2-D) half-wavelength pitch array designs to new application-specific architectures. This paradigm shift allows the horizontal integration of InP ICs, SiGe beamformer ICs, and antenna arrays to overcome thermal and integration challenges at frequencies above 200 GHz. A scalable Mills Cross Array, an aperiodic 2-D super array, and a scalable linear MIMO array will be developed using 4-channel heterogeneously integrated beamformer modules for sub-degree resolution automotive radar, 10-Gbps airborne connectivity, and near-Tb/s wireless base station, respectively. Compact, power-efficient beamformer transmitter and receiver ICs will be designed in GlobalFoundries' 9HP SiGe process, considering co-integration with InP front-end ICs. A new phase shifter architecture based on two parallel transmission lines periodically connected via digitally controlled switches will be explored for precise, calibration-free phase shifting at 240 GHz. 240-GHz InP PAs and LNAs will be designed using Teledyne’s InP 250-nm HBT process to obtain the highest possible performance and fit these in the linear half-wavelength pitch available within the package. Innovative Antenna-in-Package (AiP) substrate stack-ups, package materials including glass and polymer, interconnect/transition/antenna designs, and IC embedding will be explored for optimal electromagnetic and thermal performance. This project will be the first comprehensive study on materials, ICs, antenna-in-package, architectures, and system co-design for the heterogeneous integration of phased array modules above 200 GHz. 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 developing and evaluating T-PRACTISE (Team-Based Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education), a team-based framework for fostering engaged online asynchronous learning among diverse students, such as adult learners. These students may be pursuing higher education asynchronously while balancing myriad work-, family-, and life-related demands in online and hybrid environments. This project addresses some of the long-standing challenges in online and hybrid learning made salient by the COVID-19 pandemic and subsequent recovery. The project plans to develop and evaluate T-PRACTISE, a set of tools that integrate current e-learning technologies with online team-based learning activities. This work aims to identify and circumvent training disparities through: (1) timely identification and delivery of remedial actions to develop pre-requisite skills; (2) an innovative algorithm to determine and provide personalized dosages of training and engagement exercises; and (3) extraction and evaluation of engagement and learning/teaching outcomes across multiple iterations and time scales to elucidate possible determinants of changes in learning/teaching and engagement outcomes. This projects targets 1000 undergraduate students from online, hybrid, and in-person degree programs and is pursuing three goals. First, is to design, implement, and pilot a proof-of-concept T-PRACTISE system to reduce training and engagement disparities in an asynchronous online environment. Second, is to adapt and extend T-PRACTISE to hybrid courses with a range of environmental characteristics (e.g., student, instructor, and course). Third, is to evaluate students’ and instructors’ changes in learning/teaching outcomes, engagement, and possible moderating effects of environmental characteristics. To enhance quality control, project deliverables are refined by incorporating feedback from students, instructors, and an independent external evaluator. Efficacy of didactic activities aimed at circumventing students’ and their teams’ training disparities is evaluated based on ongoing computerized adaptive assessment results, real-time learning and engagement analytic data from T-PRACTISE and extant e-learning tools, and inclusion of students’ engagement data into the training recommendations devised by the learning algorithm in T-PRACTISE. The utility of the T-PRACTISE system will be evaluated across a variety of fully and partially online classes of different sizes for transferability and scalability. The expanded web app and resources will be made freely available to the broader research community to encourage cross-instructor, cross-departmental, and cross-institution exchanges of students’ training strategies to enhance the future of personalized and team-based higher instruction. 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

The diets of rural and Indigenous communities are impacted by contextual changes in the environments in which people acquire food. This Doctoral Dissertation Research Improvement project examines the ways that transitional food environments impact diet quality for members of these communities. Drawing on theoretical perspectives from political ecology, this project contributes to equitable public health outcomes by attending to the experiences of Indigenous and rural women and their contributions as food decision-makers in changing environments. Inclusive understandings of food environments help to inform global public health research and policymaking to promote diet quality and positive nutritional outcomes. The project also contributes to the training and education of a graduate student. Poor quality diet is a major public health burden that is responsible for an estimated 11 million deaths globally each year, and the burden disproportionately affects Indigenous communities. The role of food environments in driving dietary changes is still not fully understood. This doctoral dissertation project contributes to more complete understandings of the relationships between food environments and diets by addressing multiple, complementary research questions. First, the researchers examine how changes to food environments affect individuals’ decision-making? Second, the study elucidates the pathways by which changing food environments lead to heterogeneous outcomes in diet quality. Third, the research considers the social, political, economic and environmental factors that potentially underlie uneven and gendered experiences of food environment change. Mixed methods involving interviews, focus group discussions, geolocated participant observation and participatory mapping are used to investigate changes to individuals’ and communities’ food environments and diets. Developing community-centered methods in collaboration with Indigenous scholars to study changing food environments in rural and Indigenous contexts advances broader interdisciplinary research on diet quality. Enhanced understandings of the relationships between food environments and diets in different contexts help to foster research and policy for equitable and sustainable food system transformations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

The tremendous growth of wireless data traffic over the past decades is expected to accelerate even more in future due to increasing demands for high-speed wireless connectivity, ubiquitous network access, and end-user experience. Sub-terahertz (THz) communications, defined as above 100 GHz, are envisioned as a key technology to enable the needed wireless terabit-per-second links by leveraging the hundreds of gigahertz bandwidths available at sub-THz bands. A major challenge in sub-THz bands, caused by higher propagation loss with increasing frequencies, is the limited communication distance. An emerging technology that promises to improve wireless coverage is the active reconfigurable intelligent surface (active-RIS) that consumes low power and provides efficient control of the reflected signals in both phases and amplification. Realizing this potential will require substantial research in hardware design and prototyping of wideband RIS operating above 100 GHz, as well as novel communication and network algorithms for active-RIS-aided wideband systems, together with experimental evaluation and validation of such unique sub-THz networks with active RIS. This project focuses on the 142 GHz frequency band as a front-runner for the first sixth-generation (6G) spectrum to be allocated above 100 GHz and a top choice for future Wi-Fi spectrum allocations in the years to come. The project consists of three intertwined thrusts. The first thrust is to design and prototype a wideband liquid crystal-based RIS with a wide angular range of tunable reflection operating at 142 GHz. Starting with a design for passive RIS as the proof-of-concept at this high frequency, an active RIS design will then be realized using amplifier-integrated LC-based substrate-integrated waveguide, enabling high tunability for each RIS element. The second thrust is to design robust and efficient algorithms for optimal control of the active RIS coefficients including frequency-dependent phase shift and amplitude amplification. Novel algorithms leveraging unsupervised graph neural networks and reinforcement learning will be used to capture the underlying network interaction and to provide strong scalability and generalizability. The third thrust is to perform extensive validation using the NSF-funded open-source ray-tracing simulation tool “NYURay” for active-RIS-aided sub-THz channel simulations. In addition, the prototyped passive and active RISs will be used to conduct on-site wireless propagation measurements utilizing the wideband sliding correlation channel sounder to create a site-specific hybrid channel model for RIS-aided communication. Through various education and outreach activities to broaden participation in computing, this project will foster knowledge sharing and contribute to industry and regulatory advancements in THz communications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

Homelessness is a critical social issue in the United States, exacerbated by the complex interplay of behavioral health problems such as mental health disorders and substance abuse. This project addresses this issue by enhancing the Allegheny County Department of Human Services' (DHS) existing Allegheny Housing Assessment (AHA) tool, which prioritizes housing services for homeless individuals based on risk scores (or assessments). The primary goal of the project is to develop AHA+, an advanced version of the current tool that integrates sophisticated artificial intelligence (AI) to improve transparency and decision-making. By providing clear explanations for risk scores and optimizing service allocation, AHA+ aims to foster trust and efficiency in the delivery of housing and behavioral health services. This initiative not only aligns with the National Science Foundation's (NSF) mission to advance knowledge but also sets a precedent for the responsible use of AI in social services, potentially transforming public perceptions and applications of AI in various fields. The project focuses on two main research thrusts. The first thrust involves developing and implementing a range of automated explanation systems for the current AHA tool to enhance transparency. These systems will provide varied types of explanations, such as rule-based, example-based, and counterfactual explanations, to cater to different stakeholders' needs. User studies will be conducted with DHS staff and homeless clients to refine these systems, ensuring they are user-centric and effective. The second thrust aims to optimize the allocation of housing and behavioral health services using advanced optimization algorithms. This includes developing AI based predict-then-optimize style optimization algorithms and models that recommend the best combination of services for each client based on their specific needs, moving beyond the current ad-hoc decision-making process. By continuously engaging stakeholders in the design and evaluation of these systems, the project ensures that the AI technologies developed are both practically viable and ethically sound. Through this participatory approach, the project seeks to create a more effective and equitable service delivery model, ultimately improving outcomes for homeless individuals and families. 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

Warming Arctic temperatures are causing permafrost regions of the far north latitudes to thaw for the first time in millennia. These regions make up a large portion of the world’s northern most coastlines. Permafrost consists of frozen sediments that, when temperatures rise like they are now, causes the ice inside them to melt. Ice-like minerals, called clathrates, that abound in permafrost also decompose and release additional water and methane. The combination of liquefaction of the sediment and corresponding decrease in sediment volume causes deflation resulting in ground instability, subsidence, and accelerated coastal erosion. Erosion along Arctic coasts threatens not only coastal communities, but also installed infrastructure like roads, harbors, airfields, pipelines, and other essential elements of modern civilization. This Civic Innovation Challenge (CIVIC) planning process brings together university scientists, Arctic indigenous people living on the coast in the North Slope of Alaska and their community organizations, North Slope harbor masters, and other North Slope interested parties to co-design hazard maps that can be used to identify areas in peril, those that will become so in the near future, and areas that would be protected from the impacts of coastal erosion. Such a map will provide a much needed high-resolution tool to help improve coastal Alaskan community resilience and inform their planning and possible relocation of people and infrastructure for the future in a rapidly warming Arctic. The planning team consists of scientists from Penn State and the Universities of Alaska Fairbanks and Alaska Southeast and the following Alaskan entities: IUC Sciences LLC which represents the community of Utqiagvik; the Port Authority of North Slope/Barrow; the Office of Risk Management in the Department of Administration of North Slope/Barrow; and indigenous members of the communities of Utqiagvik, Point Lay, Wainwright, and Kaktovik. All will be part of a 3-day CIVIC planning meeting workshop that will be held at Utqiagvik to define and co-design the map and its analytical tools and user-interface. The map will provide significantly higher resolution than those already available to North Slope communities. Broader impacts of the work include development of a tool that can help inform residents and infrastructure along the North Slope of Alaska about areas at high risk and vulnerability to climate change, improve planning; protection; and relocation of assets and homes to locations safe from flooding, coastal erosion, and subsidence. Impacts also include the training of North Slope residents and land use managers on use of the map and its tools to increase climate resilience of Arctic communities, many of which are inhabited by indigenous people. It will also engage indigenous youth, using the map and its discovery tools to promote interest in, and the mastery of, science and technology concepts. The project team will gain valuable experience working with Alaskan key stakeholders, and North Slope of Alaska communities will see how the transition of foundational research to practice can achieve long-term societal benefits. This CIVIC project focuses on providing high resolution, interactive coastal hazard maps and other on-line tools to help Alaskan coastal communities improve their resilience to the impacts of climate change. More than 200 Alaska Native villages are presently suffering from coastal erosion and flooding; and thirty-one Alaskan villages currently face imminent threats of coastal inundation and need relocation. The goal of this Civic Innovation Challenge (CIVIC) planning period is to bring together key stakeholders from across the north Arctic coastal area to co-design, with the science team: elements; applications; the user interface; and data visualizations of a high-resolution, coastal, climate hazard map that would be developed and implemented in a follow-on CIVIC proposal. In the course of North Slope hazard map creation, the CIVIC team will use their experience with North Slope indigenous communities to determine the best way to engage far flung rural, low-technology, predominantly subsistence-oriented, Native Alaskan communities and deliver a tool that is useful to them and that they can use to increase their resilience to climate change. The project also establishes effective means and protocols for co-designing, with stakeholders, high-spatial-resolution Arctic coastal hazard maps that simultaneously include the three major types of Alaskan coastal hazards (i.e., coastal erosion, flooding, land subsidence). The project will also explore mechanisms that could provide sustainability and long-term integration of new data into the map. The team will use this experience to see whether the map can be used as a vehicle to determine real and long-lasting community benefits of science/community interactions. Partners in this CIVIC project include university faculty and regional and local governments, civic organizations, and Indigenous communities. Partners include Native Alaskan village corporations, the North slope Port Authority, and other North Slope stakeholders including North Slope village residents. To achieve the planning process goal, a multi-stakeholder meeting will be held in an Alaskan North Slope town to garner community-wide perspectives and work with them to co-design the high resolution hazard map and its attendant applications. This planning process will result in North Slope community access to high-resolution hazard information and technology and training, in its use, to accelerate the learning and technology implementation needed to build more climate-ready Arctic communities. This planning process will also improve the understanding of how community-based efforts can be designed to provide improved nature-based solutions to climate change and will foster and strengthen collaboration between researchers and community stakeholders, develop new collaborations and partnerships, refine the research vision to enable submission of a successful follow-on proposal that will implement the community vision, and provide information to address research questions and develop evaluation methods and measures for the follow-on project. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. It was funded by the NSF Directorate for Geosciences and Directorate for Computer Information Science and Engineering. 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

Non-technical Description: Biochemical sensors play an important role in protecting humans with applications ranging from routine health monitoring to detecting biothreats. The objective of this project is to create a materials innovation infrastructure that accelerates the discovery and optimization of biosensor materials through a closed-loop computational and experimental approach. This project builds on recent discoveries by the PIs at the Pennsylvania State University and the Rensselaer Polytechnic Institute to develop theory-informed intelligent models that guide the engineering of two-dimensional (2D) materials (specifically transition metal dichalcogenides, TMDs) as core biosensing films. Owing to high surface-to-volume ratio and tunable electronic properties, 2D materials have found unique electrochemical applications. However, sensing applications mainly rely on trial and error when making material choices. This project proposes to address this gap using a feedback loop of computational modeling, artificial intelligence (AI) modeling, scalable TMD synthesis methods, and sensor fabrication and testing. The proposed research is integrated with outreach and educational activities that target a broad range of students in STEM. Networking with industry advisory board (IAB) members will provide students with internship and employment opportunities, strengthening the future interdisciplinary R&D workforce. Technical Description: The discovery of sensor materials is a complex technical challenge that requires the convergence of multiple complementary expertise. The number of candidate materials for various bioanalytes is enormous, making it virtually impossible to search the entire materials space using only theoretical calculations and/or experiments. The aims of this project are to (1) Understand the effect of TMD functionalization and doping on nanoscale interactions with biomolecules and governing processes. (2) Develop active learning AI models to accelerate computational modeling in a closed-loop fashion. (3) Synthesize new and improved sensing materials and establish process-property-performance relationships using various characterization techniques. (4) Synthesize TMDs using scalable methods, followed by sensor fabrication and multimodal screening to determine material sensitivity and specificity to molecules through an iterative feedback loop with computational studies. (5) Create a cloud database for storage and sharing the project outcomes with the research community. While the framework will be developed and benchmarked using a set of stress biomolecules and neurotransmitters, the knowledge base and methodologies can be extended to the intelligent design of functional materials for other molecules, such as those linked to food safety, water monitoring, biodefense, and pharmaceutical agents. The research outcomes are expected to have a transformative impact on AI-guided design of materials for electrochemical sensing that may branch out to other areas, including smart coating, gas sensing, and catalysis. The proposed cloud database can have a lasting impact on academia, government organizations, materials manufacturing industries, and health diagnostic industries. 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

Research funded by this award aims to enhance current understanding of the compaction of granular soils, a critical construction process for many civil infrastructure systems. Compaction is the most common method of soil improvement for these soils. Yet, traditional compaction often relies heavily on engineering experience and post-construction quality control, leading to under or over-compaction problems in the field. This research project will provide new insights into the effects of granular soil properties and compaction equipment characteristics on compaction efficiency, which may lead to more efficient construction practices and reduced carbon footprints of civil infrastructure systems. This project is a collaborative effort between two Penn State campuses, Altoona, primarily an undergraduate institution, and University Park, a research institution. It will provide substantive research experiences to undergraduate students from Altoona, exposing them to contemporary knowledge such as sensing technology and data transmission. These experiences will enrich the engineering curricula at both campuses. In particular, the improved curriculum will benefit the Rail Transportation Engineering program at Penn State – Altoona, the nation’s first and only four-year bachelor’s degree program in railroad transportation. The central hypothesis of this research is that particle kinematics can be used as a proxy of soil compaction, rather than surface settlement, to study the state of compaction in granular soils. This hypothesis will be tested through an integrated experimental and numerical investigation. The project will involve laboratory compaction tests, which will be instrumented with geophones, accelerometers, a linear variable differential transducer, and a load cell; these instruments will record the dynamic response of soil in the compaction zone and the reaction force to the compactor due to soil-compactor interaction. In particular, wireless sensing devices, SmartRocks, will be embedded at various locations in the compaction zone to record the evolution of particle kinematics (e.g., acceleration and rotation) during compaction. The compaction test results will be used to calibrate and validate a computing model based on the idea of fusing SmartRock measurements and discrete element simulations to increase the accuracy of the simulations. The validated computing model will be used to extend the insights gained from the laboratory tests to field conditions that resemble the compaction of a moving vibratory roller compactor. This research will, for the first time, yield insights into the effect of granular soil properties, equipment characteristics, and operating frequency on the particle kinematic behavior (e.g., rotation, acceleration, and contact stress) in different zones during compaction. 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

Flooding is an increasingly prevalent, dangerous, and costly phenomenon being driven by climate change and its related incidence of events that involve extreme precipitation. Protection from flooding, especially for rural and small communities, depends on levees put up and maintained by local authorities. Many of these are not accredited by FEMA due to complications in the accreditation process and lack of the means and knowledge of local communities on how to evaluate levee integrity. Unaccredited levee systems saddle communities with high flood insurance rates and could result in potential danger to communities and the surrounding area from inadequate levee management and/or construction. This Civic Innovation Challenge (CIVIC) planning process uses rural areas in Pennsylvania as a pilot to bring scientists and rural/small communities that own levees together with engineering firms, levee evaluation experts, and state and regional stakeholder entities to co-design tools and a process that provides improved flood resilience and levee safety and management for rural communities. A goal of the project is also to help communities with unaccredited levees navigate the accreditation process. Planned deliverables include the piloting of cutting-edge, low-cost, non-invasive geophysical and geospatial testing for levee monitoring and evaluation; leveraging high-resolution flood modeling capabilities to quickly generate risk-based flood and levee indicators to inform preliminary accreditation decision; and identifying practices that can be implemented across rural communities to improve levee safety. The planning process involves co-design with stakeholders to refine the vision and co-design a process for fast-paced engagement with levee regulators leading to levee accreditation, delivering tools for better community levee management and evaluation of levee integrity, and introducing and incorporating innovations in levee monitoring and construction. Refinement of the vision and co-design of possible solutions include a multi-disciplinary science team and the Office of Government and Public Relations from Penn State, engineers and levee inspection professionals from Pennsylvania engineering firms, the Pennsylvania State Department of Environmental Protection, and community representatives from the Pennsylvania Boroughs of Evertt; Patton; and Philipsbury; and Smithfield Township. Representatives from FEMA and the Susquehanna River Basin Commission will also be involved. Broader impacts include development of online tools and accessible data analysis and visualization products to help rural communities monitor and safely manage their levee systems. It also provides information needed to ensure local levees are safe and can control climate-driven flooding, potentially leading to levee accreditation and reduced flood insurance costs. The goal of this CIVIC planning period is to bring together key stakeholders from across Pennsylvania and from the levee regulatory/accreditation sector to co-design methods and the elements, applications, user interfaces, and visualizations using low-cost, non-invasive satellite/geospatial and geophysical techniques to improve the safety and integrity of non-governmentally operated small community and rural unaccredited levee systems. The activity involves planning for the creation of a digital, levee, diagnostic tool and a story-map-based community guide for levee maintenance, evaluation and accreditation. The tool will allow users to examine existing levees in Pennsylvania and explore the associated levee data and indicators, such as risk of overtopping, projected cost of accreditation, cost of insurance premiums if accredited, etc. It will also describe the testing, modeling and community engagement activities for each community partner so other communities can better understand accreditation criteria, technology, engineering innovations, and cost-saving options. The tool will use the Google Cloud Platform, Python Dash, and geographic information systems (GIS) hosted online for free by Penn State Cloud Services. This planning process and the engaged group of stakeholders will work together to provide community access to high-resolution, remote sensing, geospatial, and geophysical and information that will allow improved monitoring, safety, and management of local levee systems as well as a path to levee accreditation. This planning process is designed to improve the understanding of how collaboration between communities and government entities can solve problems impacting small and rural communities impacted by flooding brought on by climate change. It will foster and strengthen collaboration between researchers, community stakeholders, and regulators, develop new collaborations and partnerships, refine the research vision to enable submission of a successful follow-on proposal that will implement the project vision and provide information to address research questions and develop evaluation methods and measures for the follow-on project. This project is in response to the Civic Innovation Challenge program’s Track A. Climate and Environmental Instability - Building Resilient Communities through Co-Design, Adaption, and Mitigation and is a collaboration between NSF, the Department of Homeland Security, and the Department of Energy. The grant was co-funded by the NSF Directorate for Geosciences and the Directorate for Engineering. 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

Understanding the impact of habitat disturbances on wildlife health is crucial in an era of global change. This research focuses on how disruptions in habitat connectivity affect microbial communities that live on amphibians. These microbiomes help protect amphibians from one of the most devastating pathogens of vertebrates, a skin fungus that is lethal to hundreds of species globally. By studying how frogs recruit these protective microbiomes from their environment, we aim to discover how environmental disturbance and shifts in habitat connectivity influence their ability to resist fungal infections. This work will provide new insights into the health of wildlife populations in disturbed and fragmented landscapes, helping us better predict and respond to environmental challenges and disease emergence. The researchers will work with the One Health Microbiome Center at Penn State to develop advanced ecological modeling tools and train the next generation of scientists. Additionally, they will partner with the Penn State Office of Science Outreach to organize a summer camp for middle school students, to immerse students in the fascinating world of microbes through fun, hands-on microbial ecology activities. A cornerstone of the proposed research is to examine the adaptive microbiome principle, a deterministic process where previous pathogen exposure generates host-associated microbial communities that are enriched with pathogen-inhibiting function, which in turn enhances host resistance in subsequent pathogen exposures. Recent findings, however, also support the hypothesis that individual-level habitat use and the nature of the environmental pool of microorganisms play a key role in assembly of host-associated microbial communities through stochastic processes. The balance between these two potential mechanisms is unresolved. The two focal aims of this research include: 1) experimentally testing the adaptive microbiome principle by housing amphibians in environments with distinct microbial pools and introducing varying levels of the focal fungal pathogen under controlled laboratory conditions, and 2) investigating how habitat fragmentation, environmental microbes, and pathogen exposure affect skin microbiome assembly and function in amphibians along their natural movement paths in field conditions. These aims, combined, will enhance our understanding of how wildlife fauna selectively filter protective microbiomes in the face of increasing environmental change and pathogen pressure. 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

Geologic storage of carbon dioxide (CO2) can be used to reduce CO2 in the atmosphere by removing it from the surface carbon cycle. This Industry-University Cooperative Research Center for geologic CO2 Storage Modeling, Analytics, and Risk Reduction Technologies (CO2-SMART) creates a multidisciplinary program that accelerates the safe and cost-effective sequestration of CO2 in the deep subsurface, at scale. This is done via use-inspired, basic research and workforce development in an area needed by industry. CO2-SMART is a partnership between the Pennsylvania State University and the University of Southern California. The Center focuses on advancing the current understanding of the complex processes that are triggered during and after CO2 injection into geologic formations. It develops advanced modeling and other activities that improve the safety, efficiency, and economics of subsurface CO2 sequestration operations and develops a pragmatic policy framework for large scale deployment of geologic CO2 storage. The Center brings together leading faculty, researchers, and their students from across the fields of engineering, machine learning, computer science, statistics, subsurface flow, simulation, geoscience, energy policy, and economics at the two participating universities. It enables collaboration between university personnel and CO2 sequestration field operators and carbon management regulators to develop synergistic collaborations that allow key stakeholders to tackle the challenging problem of CO2 sequestration. Broader impacts have signifcant societal, energy security, and public health implications. These include development and training of the next carbon sequestration workforce, including developing engineers and scientists as technical leaders who are well-versed in implementing and managing large-scale geologic CO2 storage projects. The Penn State Site will work to broaden participation in the CO2 sequestration industry through both new course and credential programs and through relationships with local community colleges and universities. In addition, CO2 SMART's benefits can be extended to other subsurface flow systems including those related to groundwater, geothermal, and hydrocarbon resources, all of which share similar technological challenges. An Industry Advisory Board, consisting of companies, government agencies, and other interested parties will help guide Center research to ensure it is responsive to the evolving needs of the carbon economy and companies implementing geologic-carbon storage. The CO2-SMART Center aims to enable and accelerate safe, reliable, efficient CO2 storage and implementation strategies through use-inspired research relevant to the carbon sequestration economy. It's activities include workforce development and public outreach in this important area. The Center achieves its goals through collaborative, pre-competitive, basic research in the following key thrust areas: (1) Site screening and characterization, including fluid, rock, and fracture properties; (2) Multiscale and multi-physics modeling/simulation for prediction of CO2 displacement in storage aquifers; (3) Cost-effective reservoir monitoring and characterization, including geophysical, geochemical, and geomechanical monitoring for high-resolution imaging; (4) Machine learning and data analytics for flexible and efficient workflows, including multiphysics data processing, predictive modeling, and decision support tools; (5) Risk assessment and uncertainty quantification for complex multi-physics subsurface storage systems that to informs risk mitigation strategies; and (6) Improving the economics of geologic carbon sequestration through optimization and field development planning. As a complement to University of Southern California expertise, the Penn State Site contributes its expertise in characterization of CO2 storage sites using a combination of geostatistics and data integration tools that employ AI assisted workflows. It also carries out laboratory investigation of fundamental properties and processes associated with in-situ mineralization and fracture and fault reactivation and well injectivity as well as develops in-situ monitoring protocols that result in novel, cost-effective, geophysical techniques. This Site informs policy development that integrates economics, uncertainty quantification, and risk assessment in the CO2 sequestration space. In terms of workforce training, Penn State will integrate geologic CO2 sequestration into undergraduate courses and develop courses that emphasize both the technical aspects of carbon sequestration as well as the economic and regulatory aspects. Courses will be packaged as in-person and online certificate or credentialling programs that benefit both students and working professionals. Penn State will implement education and outreach activities targeting local school districts and work to broaden participation in STEM. 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

Machine Learning (ML) has pervaded almost every aspect of current-day society. With ML touching the lives of non-expert users, a critical need for transparency and accountability in ML systems has arisen. For example, using ML systems as part of the hiring process where the people in charge do not know exactly how best potential candidates for a position are picked. This project addresses this issue by focusing on developing counterfactual explanations (CFEs) for ML models, a technique for providing easy-to-understand and actionable explanations (to end-users) for the deployed ML models. Unfortunately, there are still major limitations with current state-of-the-art CFE techniques which have impeded their widespread adoption in real-world contexts. This project focuses on addressing these limitations by developing secure, robust, and user-driven prediction aware CFE techniques for ML models, which are essential for providing actionable recourse to marginalized populations that are negatively affected by algorithmic decisions. The project's broader significance lies in its potential to protect intellectual property, maintain trust by ensuring recourse recommendations remain valid despite model updates, and incorporate stakeholder feedback into an explanatory design. This aligns with NSF's mission to advance knowledge and education in science and engineering, while promoting fair and transparent use of technology. The impact extends to various domains, including agriculture, healthcare, etc., where improved transparency and accountability of ML systems can lead to better decision-making and improved outcomes for underrepresented groups. Counterfactual explanations (CFEs) for Machine Learning (ML) models have received a lot of attention due to their ability to provide actionable recourse to marginalized populations in a wide variety of domains. While most CFE techniques are post-hoc (i.e., they generate explanations for pre-trained black-box ML models), a recent line of research proposes prediction-aware CFEs that make a novel departure from the prevalent post-hoc paradigm. Unfortunately, there are critical research gaps that need to be tackled before the promise of prediction- aware CFEs can be truly realized - (i) These techniques can be exploited by adversaries to extract proprietary ML model details which could lead to intellectual property theft. (ii) These techniques generate explanations which often grow stale as the enterprise continuously updates their proprietary ML model, which prevents enterprises from honoring these stale CFEs. (iii) These techniques do not allow stakeholders to express feedback about the CFEs provided to them. To address these research gaps, we propose to develop a new suite of secure, robust, and end-user driven prediction-aware CFE techniques that increase the usability of prediction-aware CFE systems. Our algorithmic tools and approaches will leverage and build upon techniques from adversarial machine learning, bi-level optimization techniques, deep learning, game theory, and security. Additionally, the project includes developing an interactive smartphone application to facilitate stakeholder interaction with CFEs, enhancing accessibility and usability. The research plan involves rigorous evaluation using benchmark datasets and real-world user studies in collaboration with non-profits to measure the effectiveness of the proposed techniques in practical scenarios. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-10

This project aims to serve the national interest by investigating the structural, systemic, and social barriers significantly impacting STEM students' participation in Innovation Competitions and Programs (ICPs). Student ICPs are central to college-level innovation and entrepreneurship ecosystems–fostering students' meaningful STEM-based collegiate experiences and enhancing their content-rich skill development, career readiness, and social connections. Despite these benefits, a noticeable discrepancy exists in ICP participation between STEM undergraduates who have been historically underrepresented and those from student groups dominating STEM fields. The project intends to advance equity in innovation ecosystems by uncovering the structural, systemic, and social barriers and their impact on students in an area that has been understudied. Additionally, the project proposes to provide outreach and training activities to help organizers, mentors, and advisors redesign ICPs to be more inclusive for all students. This transformation aims to enhance underrepresented STEM students' career readiness and participation in innovation and entrepreneurship ecosystems, promoting inclusivity and robust engagement. The goals of this collaborative research project between the Pennsylvania State University (PSU) and North Carolina Agricultural and Technical State University (NCAT) are threefold: (i) to advance the understanding of the structural, systemic, and social barriers that limit ICP participation of underrepresented student groups; (ii) to validate a theoretical framework based on the Situated Expectancy-Value Theory to explain the complex relations among these barriers and student perceptions and choices toward ICPs; and (iii) to test the efficacy of interventions designed to increase participation in ICPs by lowering these barriers through innovative scenario-based field experiments. The project employs mixed research methods to identify barriers to ICP participation of students from underrepresented groups, develop a theoretical model to explain how these barriers affect underrepresented STEM students, and test interventions to mitigate their negative impact. Data and resulting trends will be interpreted through a participatory meaning-making process that engages stakeholders with diverse perspectives and voices. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through the Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

The rapid advancement of generative artificial intelligence and large language models is transforming how people process data, gather information, and acquire knowledge. Especially, in the era of information explosion, people are increasingly resorting to large language models to summarize documents to capture important information and make decisions. However, despite the prevalent usage of large language models, they have been criticized for inadequate alignments with humans by ignoring user needs of styles and contents, displaying bias by neglecting opinions from certain groups, and making factual mistakes on human knowledge and truth. The goal of this project is to advance trustworthy summarization by centering design, development, and deployment on humans in terms of user preferences for controllability, social perspectives for fairness, and human knowledge for factuality. It enlightens the research imperative of integrating human factors into large language models for trustworthy artificial intelligence and diverse societal impacts in domains such as scientific discovery, legal democracy, and public health. This project also initiates several aspiring education and outreach activities supported by project research outcomes to involve, mentor, and empower female, underrepresented, disabled, and interdisciplinary students. This research advances state-of-the-art controllable, fair, and factual summarization for efficient information gathering and knowledge acquisition by establishing a comprehensive set of infrastructure including algorithmic foundations, technical innovations, public benchmarks, and integrated platforms. The research plan consists of four thrusts to establish rigorous definitions of new tasks, reliable metrics, and novel models. First, the project incorporates fine-grained user preferences by customizing summaries based on their compositional requirements of contents and styles. Second, the research embraces voices from different social groups by generating fair and unbiased summaries to comprehensively cover diverse perspectives and conflicting opinions. Then, the research honors human knowledge by summarizing documents with multimodal information including unstructured text, structured knowledge of tables and citations, and visuals of figures and plots. Finally, the research deploys an integrated, interactive, and visualizable summarization platform to assist users with efficient and accessible document understanding. Through these innovations for trustworthy human-centered summarization, the research thrusts collectively advance usable, trustworthy, responsible, and safe artificial intelligence that operates under user control, reflects social norms, and honors human knowledge. 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

Nationally, a significant investment is made each year in genome sequencing. This investment is critical for expanding our understanding of biology, enhancing and optimizing agricultural output, as well as improving human health. However, sequencing by itself does not translate into these advances. This project aims at developing critical infrastructure components necessary for interpretation of genomic data. As a result of this work, cutting edge tools, workflows, and tutorials will become available to a much wider audience of biology researchers. Training is a dedicated aim, and tools will be made usable, as well as comprehensible for non-specialist users. Importantly, the project will also leverage public NSF-funded cyberinfrastructure to run these tools at scale. This project is one of the first in which high-quality genome sequence assembly workflows will become available for immediate use to a wide audience of biologists. Combined with comprehensive documentation and tutorials, the infrastructure deployed will have a profound effect on the quality and number of genome assemblies, translating into downstream discoveries. It will streamline access to information important for basic research, including for biodiversity research, molecular genetics, evolutionary analyses, and biogeography, helping to ensure planetary resilience. Making assembly and alignment methods widely accessible will contribute significantly to the bioeconomy, ensuring that national investments on sequencing projects provide return in the form of new knowledge important for basic and applied research. Relying on public computational infrastructure will open these tools and workflows to immediate use by all researchers, whether or not they have access to computing and domain expertise, fostering equitable genomics. Development of a dedicated toolkit for manipulation and analysis of whole-genome alignment will make newly created as well as already existing alignments much more useful for downstream analyses; any researcher will be able to study the evolution of their gene/genes of interest. Finally, the comprehensive portfolio of interactive training materials we will develop will be the first training effort in comparative genomics with a global reach. 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

Plastic pollution is present in every corner of the planet and is routinely ingested by countless species. While there has been increasing public attention toward plastic pollution, little attention has focused on the unseen “dark matter” of the plastics problem: the thousands of chemical additives incorporated into plastic. Despite the prevalence of these additives, their synergistic and cumulative impacts across biological scales are poorly understood. The durability, persistence, and complexity of plastic additives in our ecosystems make plastic additive pollution an open-ended and intractable “wicked” problem for global security. Aligned with societally important goals of protecting the environment and promoting environmental sustainability, this Growing Convergence Research (GCR) project will illuminate the dark matter of plastic additive waste and alleviate the impacts of this waste through a Strategic Initiative to Mitigate Plastic Additive Pollution. This project will bring together a convergent team of experts in molecular and cell biology, environmental toxicology, community ecology, high-throughput chemical screens, environmental chemistry, materials science, plastic policy, environmental law, science education, and community engagement. This project comprises two phases. Phase I will focus on determining the impacts of plastic additives across biological scales through the following activities: 1) characterization of the impacts of plastic additives on cells, 2) organisms, and 3) ecological communities, 4) an assessment of the regulatory landscape of plastic additives, and 5) community level ground-truthing to assess product use and potential additive exposures in communities. Phase II will integrate knowledge gained in Phase I to develop and pilot mitigation strategies to: 6) prioritize additive combinations in need of the most urgent mitigation, 7) model novel regulatory interventions; and 8) test policies and create action plans for convergence on plastic additives. The broader impacts include deep engagement with external stakeholders across sectors to implement innovations at the local scale to reduce plastic pollution. The project will also empower students underrepresented in STEM and other to take action against environmental pollution in vulnerable 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

This BCSER Individual Investigator Development (IID) project will build the Principal Investigator's, an Associate Teaching Professor of Physics, capacity to carry out high-quality STEM education research, with the goal of improving student learning in on-line STEM courses. In addition, to the planned professional development, the project will support a pilot STEM education research project that investigates on-line learning physics course design factors and student factors that impact learning outcomes. The pilot is designed to result in new knowledge about these factors in order to influence the design of on-line courses which are more and more common in STEM and to increase student learning and success in these asynchronous courses. Asynchronous online education allows students to choose the time to do their class work, filling a crucial need for students that are active military, that have full-time employment, family-care duties, or special accommodation needs. The PI will investigate the changing student population and the impact of different course elements on academic outcomes such as pre-post proctored conceptual tests. The findings from the interviews, focus groups and surveys will identify the factors that the students perceive as enablers or barriers to their success. This will lay the foundations for quantitative studies on how online course design impacts students' academic outcomes in asynchronous online physics courses with particular attention to non-traditional learners. Project findings will be shared through workshops and presentations at conferences. The success of this project will be assessed through regular meetings with the advisory board. The project is supported by NSF's EDU Core Research Building Capacity in STEM Education Research (ECR: BCSER) program, which is designed to build investigators' capacity to carry out high-quality STEM education research. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Awards · FY 2024 · 2024-09

This EArly-concept Grant for Exploratory Research (EAGER) project focuses on establishing rigorous foundational artificial intelligence and network science-based strategies to represent data, build search strategies, and help form clusters of Small and Medium Manufacturing Enterprises (SMEs) to meet production demands. SMEs form the backbone of our country’s economic prosperity, community welfare, and self-sufficiency. Strengthening the USA’s manufacturing base will help in the nation to become less dependent on global supply chains, thus helping national security and self-sufficiency. However, finding and selecting relevant SMEs for a specific product currently requires manual consultation of databases, manual collation of data, and compiling information which is not ideal in the age of information explosion. This research will help establish the scientific foundation for representing SMEs data, provide for distilling the relevant information about the SMEs of user’s interest, and facilitate building collaborations among SMEs. By providing such methodologies, this effort will help enhance an indigenous manufacturing base. This award will also result in enhanced national security by providing efficient provenances for all manufactured products. Many SMEs in the USA tend to have 3rd or 4th generation workers and are predominantly rurally located. The efforts from this award will help in establishing a platform for the manufacturing ecosystems for the future wherein diverse SMEs irrespective of location and scale, will have an equal opportunity to participate. The research focus is on using a network science-based representation to connect SMEs as graph database formalisms that offer representation power, help build efficient search mechanisms, and offer intuitive methods to connect SMEs to fulfill the demand that cannot be met by a single provider. Machine learning and economic theory based foundational algorithms resulting from this research will help in creating optimal search and coalition formation among SMEs. The scientific foundations of this research will be developed through SME inspired use cases, thus addressing both theoretical and engineering considerations to make this research generalizable to all SMEs. By the nature of design, the methodologies will help build a high level of data security and long-term sustainability of the ensuing platform. This award will address: 1) data models for representing SMEs, 2) search mechanisms to find SMEs based on specific contexts, 3) composition of SMEs to meet the requirements of larger enterprises in an economic and sustainable manner, and 4) federated learning schemes to recommend relevant SMEs for specific contexts. Graph databases offer a unique representation formalism that is dynamic, flexible, and scalable, making them suitable for addressing these research problems. Additionally, game theoretic and auction-based mechanisms will be used to compose SMEs and help in supplier selection. Real-time issues, dynamic updates, and scaling up of computational considerations are considered in this research. 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

Cosmology leaps to a new level each time we expand the distances-measurement horizon. The existence of the extragalactic Universe and its expansion was revealed only after reaching out to a few Mega light-years; subsequent extension of the horizon to a few Giga light-years led to the discovery of the Universe’s accelerating expansion. Cosmic acceleration indicates that new physics must operate on even greater cosmological scales, and astronomers coined the term Dark Energy to address this problem. Scientists at Penn State University propose to address the Dark Energy problem using the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) galaxy survey data, which observed 1.5 Million galaxies within a cosmological volume (400 cubic Giga light-years) at tens of Giga light-years away. By scrutinizing the clustering of these ancient galaxies, the team will measure the Dark Energy density at the earliest time test whether Dark Energy varies in time. With the same dataset, the team will also study the physics of the early Universe that generated the initial seeds for the observed large-scale structure. As part of this project, the team will educate new Ph.D. students and create undergraduate research opportunities. The team will also engage in outreach activities at Penn State University by presenting public lectures during AstroFest and AstroNight and by organizing the Neighborhood Workshop on Astrophysics and Cosmology. The team will measure the Dark Energy density at high redshift (1.9<z<3.5) using both geometrical (Baryon Acoustic Oscillations and Alcock-Paczynski test) and dynamical (Redshift-space distortion) measurements using the power spectrum and bispectrum of Lyman-alpha Emitting galaxies in the HETDEX survey. Also, the team will measure the linear and quadratic bias as well as the scale-dependent bias due to the local type of primordial non-Gussianity, which will be a smoking gun signal for the inflation model beyond the conventional single-field slow-roll inflation. For the statistical analysis, the team will use the forward-modeling technique using GridSPT implementation of the standard perturbation theory to incorporate various real-world issues caused by the variations in observational conditions and to estimate the covariance matrix for the galaxy power spectrum and bispectrum. The analysis includes thorough systematic studies for selection bias, line contamination, as well as survey window function effect. 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.