Data management plan for the grant, "Solid-State Additive Manufacturing of Durable Aluminum-Cerium Alloys for High-Temperature Aerospace Structural Applications."

Data management plan for the grant, "Variable Conductor Radiator for CO2 Deposition in Deep Space Transit"

Data management plan for the grant, "In-Situ Monitoring for Quality Assurance and Machine Learning in Direct-Write Additive Manufacturing of 5G RF Electronic Ceramics." The principal near-term objective of the PI is to use in-situ monitoring to define the processing rules that determine the microstructure, macrostructure, and high-frequency dielectric response of electronic ceramic materials for advanced RF “grown” by laser-assisted direct-write AM. In-situ measurements will be correlated and validated with ex-situ characterization, and these data will iteratively guide synthesis and processing optimization. In parallel, the experimental measurements will be used as inputs for the physics-based process modeling. Predictions from the experimentally validated models will in turn: 1) guide synthesis and processing optimization and, 2) train and constrain ML. Thus, quality assurance is achieved.

Data management plan for the grant "RINGS: Mobility-driven Spectrum-Agile Resilient mmWave Communication Links for Unmanned Aerial Vehicle Traffic Management in the Sky." As the unmanned aviation industry moves towards Advanced Air Mobility (AAM) services, air taxis and air ambulances are expected to become a reality in near future. Handling such a high volume of unmanned air traffic requires innovative solutions for enhanced situational awareness in the airspace. The US Federal Aviation Federal Aviation Administration envisions air tracks specifically reserved for AAM vehicles at altitudes ranging from 500 ft to 2000 ft. Air tracks cross one another at intersections and vehicles may need to share the airspace with other manned and unmanned aerial vehicles. This requires coordination among the vehicles especially during close encounters. Such a coordination requires highly reliable communication links which serve as a substitute for traffic signals on the roads. This proposal addresses this key knowledge gap by investigating strategies for establishing reliable and robust Vehicle-to-Vehicle (V2V) communication links to support AAM services. This project investigates spectrum-agile millimeter wave-based tunable beamforming strategies needed for establishing reliable and robust V2V communications links to support autonomous flight operations in air corridors along with the supporting radio frequency and mixed signal …

Data management plan for the grant, "Increasing Skills in Accessibility through Integration in the Undergraduate Computer Science and Information Technology Curricula."

Data management plan for the grant, "CAREER: Shape Memory Polymers as Biomaterial." This CAREER projectaimsto elucidate the underlying mechanism of the plasticization-induced shape memory effect of thiol-enebased polymers. The model application for this material will be a heat shrink tubing that can shrink at bodily conditions (37° C and simulated body fluids) and can be used to seal colonic anastomosis. The specific three aims are to (1) Systematically investigate the effect of crosslink-density and chain extender length on theplasticization-induced shape memory effect of thiol-enebased polymers. Mechanical and thermomechanical measurements inside simulated body fluids will be used to assess shape memory properties and structure-property relationships. (2) Understand the relationship between material thickness, degree of shape-programming, and radial recovery forces of tube-shaped SMPs to determine optimal design parameters for sufficient shape recovery using the heat shrink tube model. (3) Demonstrate the functionality of a biomedical heat shrink tube that utilizes the plasticization-induced shape recovery through an ex vivo colon anastomosis model and quantify mechanical and sealing properties.

Data management plan for the grant, "Collaborative Research: CyberTraining: Pilot: Research Workforce Development for Deep Learning Systems in Advanced GPU Cyberinfrastructure." This project aims to develop a novel set of interactive training materials, including hands-on lecture modules, invited research talks from renowned researchers, and an interdisciplinary collaborative project in an intensive workshop, integrating a wide variety of advanced and inter-connected techniques employed by research workforce for deep learning (DL) systems in advanced GPU cyberinfrastructure (CI). Specifically, this project focuses on training seniors, graduate students, and researchers on how advanced GPU CI can be efficiently utilized and improved to enable high-performance DL systems for data-intensive DL applications in geoscience (GS) and computer science and engineering (CSE) research. The goal is to foster future CI users and contributors to adopt, develop, and improve advanced GPU CI for DL systems in their research.

Data management plan for the grant, "CPS: Medium: Integrating sensors, controls, and ecotoxicology with decoupled aquaponics using brackish groundwater and desalination concentrate for sustainable food production." This project aims to develop a cyber-physical system (CPS) testbed of integrated sensors, controls, machine learning, and ecotoxicology tools to engineer sustainable food production systems based on aquaculture using brackish water. The testbed includes an automated recirculating aquaculture system integrated with a real-time ecotoxicology system, implementing the system using desalination concentrate to demonstrate that brackish groundwater desalination costs can be offset by using its byproducts for profitable food production. This project will engage students of the local communities in three major ways: developing an exhibit and activity emphasizing interdisciplinary CPS research conducted during the academic year, offering summer research experiences to students from underrepresented groups, and participating in STEM outreach events targeting underrepresented groups.

Data management plan for the grant, "Mechanoregulators of Nanoparticle-Cell Interactions at Tissue Interfaces."

Data management plan for the grant, "CAN23-161, Hierarchically structured ISRU derived composites."

Data management plan for the grant, "Collaborative Research: Supercritical Fluids and Heat Transfer - Delineation of Anomalous Region, Ultra-long Distance Gas Transport without Recompression, and Thermal Management."

Data management plan for the grant, "Mechanochemical Reconstruction Principles for Two-Dimensional Material Adaptation to Applied Stresses." This grant focuses on understanding how 2D materials change their structure and function under loading and shear stresses. For this, we will investigate the fundamental origins of solid-state interactions, unraveling the processes occurring at contacting surfaces during sliding, why some materials show better stability than others, and how one can use these novel 2D materials for mitigating friction and wear and enabling superlubricity.

Data management plan for the grant, "CAREER: Manufacturing of Mechanically Stable Nanoporous Ceramic Structures Via Selective Infiltration of Polymer Templates." This Faculty Early Career Development (CAREER) grant from the National Science Foundation supports fundamental research to elucidate a new strategy of manufacturing nanoporous ceramic structures with controllable structure and composition and programmable mechanical stability. The specific goal of this research is to discover processing-structure-property relationships in ceramic coatings and heterostructures by providing fundamental insights on the mechanism of liquid phase swelling-based infiltration of spin-coated polymer templates with inorganic precursors and defining the rules that control the resulting structure and, thus, access to various materials surfaces and interfaces.

Data management plan for the grant, "High-Contrast Elasto-Metamaterial Photonics for Adaptable and Stable Lasing."

Data management plan for the grant, "SaTC: CORE: Small: Probing Fairness of Ocular Biometrics Methods Across Demographic Variations."

Data management plan for the grant, "Research Training Pathway for Underrepresented Minority Students in Advancing Materials and Manufacturing for Fusion Power."

Data management plan for the grant, "CAREER: Understanding the Role of Nanoprecipitates in Advanced Metastable Titanium Alloys."

Data management plan for the grant, "Tunable Plasmonic Multispectral Meta surfaces." This project investigates the fundamental light-matter interactions and light emission tunability of 2D materials augmented by plasmonic gratings. A key application of our fundamental research is the development of the next generation of optical transistor that use photons to transmit signals containing digital information.

Data management plan for the grant, "CICI: UCSS: Secure Containers in High-Performance Computing Infrastructure." Ensuring the security and privacy of high-performance computing (HPC) infrastructures is of utmost importance due to their handling of sensitive data and critical scientific computations. HPC infrastructures commonly employ containers, which provide lightweight and isolated environments for running applications. Nevertheless, containers in HPC infrastructures encounter security challenges, including insecure container images and vulnerabilities related to isolation. Existing container image scanners face a major challenge of low coverage, while current container runtimes struggle to ensure both security and performance for HPC workloads simultaneously. This project addresses these challenges by developing secure containers specifically tailored for HPC infrastructures. The project introduces innovative solutions, including the development of an efficient image vulnerability scanner and a secure container runtime.

Data management plan for the grant, "Collaborative Research: CIF: Medium: Fundamental Limits of Cache-aided Multi-user Private Function Retrieval." This project is motivated by the need to efficiently execute complex queries on massive databases in a way that minimizes the use of communication resources while preserving the privacy of the entity that initiated the query. Such queries are functions of the data points that are stored at remote servers. This project develops a principled and holistic framework for the problem of privately retrieving, at distributed cache-aided nodes, the output of functions based on both data that is locally computed and data that is received from multiple servers.

Data management plan for the grant, "Collaborative Research: SHF: Small: LEGAS: Learning Evolving Graphs At Scale." This project aims to improve the computation efficiency of graph neural networks (GNNs), which are an emerging class of deep learning models on graphs, with many successful applications, such as, recommendation systems, drug discovery, social network analysis, and code vulnerability detection. This project aims to to design an efficient GNN framework via algorithm and system co-design for both static and dynamic graphs.

Data management plan for the grant, "CAREER: Developing a Flexible Serverless Multimedia Streaming Cloud Platform."

Data management plan for the grant, "Collaborative Research: IRES Track I: Wireless Federated Fog Computing for Remote Industry 4.0 Applications."

Data management plan for the grant, "Collaborative Research: Engaging Blind and Visually Impaired Youth in Computer Science through Music Programming."