Data management plan for the grant, "Collaborative Research: Topological Defects and Dynamic Motion of Symmetry-breaking Tadpole Particles in Liquid Crystal Medium."

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: DMREF: AI-enabled Automated design of ultrastrong and ultraelastic metallic alloys"

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 a NSF grant with a name that has not been determined.

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."

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

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

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, "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, "Understanding the Role of Residual Stress Gradients on Plastic Strain Recovery in Nanocrystalline Thin Films."

Data management plan for the grant, "Ex situ and in situ Investigation of the Thermomechanical Behavior of Shape Morphing Materials from Room Temperature to Ultra-High Temperatures."

Data management plan for the grant, "Graduate Research Fellowship Program (GRFP)" for Ali Yar Khan.

Data management plan for the grant, "2023-GenCyber-University of North Texas."

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, "High-Contrast Elasto-Metamaterial Photonics for Adaptable and Stable Lasing."

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

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, "Protective Thermal Electro-Chromic Coatings (ProTECC) for Lunar Exploration."

Data management plan for the grant, "New approach based on enzyme stimulating of peptides for targeting drug resistance breast cancers." In this project, we propose the development of selective self-assembling peptides that form nanofibers via a self-assembling process upon the action of Eyes absent (EYA) enzyme, specific to drug resistance Triple Negative Breast cancer cells (TNBC). The objectives are: Aim 1: Design and synthesize self-assembling peptide substrates for EYA enzymes; Aim 2:Determining the efficacy of peptide substrates for inhibiting TNBC in spheroid 3D cell cultures. The correlation between the enzyme kinetic and the activity of nanostructures for targeting EYA will be evaluated. Aim 3: The apoptosis response of the TNBC cells will be determined. This study will lead to finding the potent peptide substrate and the effective dose for inhibiting TNBC cells with apoptosis cell death.

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, "SaTC: CORE: Small: Probing Fairness of Ocular Biometrics Methods Across Demographic Variations."

Data management plan for the grant, "SaTC: CORE: Small: Customizable Geo-Obfuscation to Protect Users' Location Privacy in Mobile Crowdsourcing."