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, "CAREER: Fundamental Limits of Cryptographic Primitives Through Network Information Theory." Research project studying the fundamental limits of a diverse array of cryptographic primitives through network information theory and coding tools. The project takes an information theoretic view of the investigation of the fundamental limits of cryptographic primitives. The project is expected to unveil theoretical and practical insights into cryptographic primitives, and enhance the understanding on their fundamental limits.

Data management plan for the grant "Integrating multi-omics datasets to infer phenotype-specific driver genes, regulatory interactions and drug response." Research to develop open source integrative computational tools that perform secondary analysis of publicly available multi-omics biological, clinical and environmental exposure datasets to infer context-specific regulatory interactions and modules, and to predict disease associated genes and patient-specific drug response.

Data management plan for the grant, "New approach for identification pHFO networks to predict epilleptogenesis." About 40% of epilepsy patients fail to control seizures after treatment, and currently there is no treatment that can prevent epilepsy. The goal of this study is to perform multi-scale electrophysiological investigations combine with advanced computational algorithm development to understand the characteristics of the pathological brain networks during the latent period of epilepsy. The findings of this project will lead to a better understanding of the network mechanisms of epileptogenesis and suggest novel approaches to prevent the process of epileptogenesis.

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, "GenCyber Teachers Camp." The goal of the UNT GenCyber Teacher Camp is to increase the cybersecurity expertise of middle and high school Computer Science teachers so that they can introduce cybersecurity curriculum into their classrooms. We will recruit 20 teachers who teach Computer Science and STEM-related middle/high school courses. We will create modules that teachers can use to create positive environments for students and thus to motivate them to gain interest in cybersecurity.

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, "Collaborative Research: Research Infrastructure: CCRI: ENS: Enhanced Open Networked Airborne Computing Platform." The project aims to develop an enhanced open networked airborne computing platform to facilitate the design, implementation, and testing of an airborne computing platform that seamlessly integrates control, computing, communication, and networking. The project will provide community support based on the flywheel model, and to attract and engage community users through organized support from four regional sites in the nation, hands-on workshops, testbed access, and technical user services.

Data management plan for the grant "Development of Genetic Sensors and Circuits for Creating Novel Cellular Behaviors." This research is expected to advance the capability to engineer organisms for biomedical uses. Specifically, the outcomes of this project include design principles for engineering regulators from different protein families, an extensive set of genetic sensors for detecting a broad range of signals, and novel genetic circuits that address uprising problems in biomedical fields. It uses a novel multidisciplinary approach to enhance the health of the nation by creating tools that facilitate both medical-related discoveries and the implementation of new strategies for biomedical applications.

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 "Modulating 3D Cellular Connectivity Via Spatially-Controlled Programmable Bonding." This project seeks to demonstrate proof-of-concept for technology that allows one to systematically place cells on substrates to create complex 3D assemblies with precise control over individual cellular interactions. The technology generated within this proposal will open new avenues for studying multicellular communication pathways, stem cell differentiation, and understanding developmental processes. Spatially-defined cell-cell communication plays a critical role in numerous disease and developmental processes that include osteoarthritic degeneration, cancer metastasis, and organ regeneration.

Data management plan for the grant "Nuclear Safety Testing and Analysis Research (NSTAR) Fellowship Program at the University of North Texas." The proposed NSTAR fellowship program at UNT aims to reinforce the workforce in the nuclear energy safety community by engaging the selected fellowship students (fellows) with interdisciplinary education and research. The overarching theme and research focus of the proposed NSTAR program is acoustics-based non-destructive testing and analysis and nanoparticle processing for thermal management applications. An interdisciplinary research team will investigate these research areas with backgrounds in mechanical engineering, material science, and/or physics. The program will fully support two fellows over four years including full tuition and stipends.

Data management plan for the grant "Collaborative Research: CCRI: Planning: A Multilayer Network (MLN) Community Infrastructure for Data,Interaction,Visualization, and softwarE(MLN-DIVE)." Research relating to creating a community infrastructure for researchers using multilayer networks (MLN). This project uses a formally established network decoupling approach to perform various aggregate analysis (community, centrality, substructure detection, etc.) using individual layers and composing them. The broader impact of this planning project is to provide meaningful and appropriate analysis tools that are grounded in theory to a broad range of applications from different domains. The focus is on facilitating the mainstream use of multilayer network analysis in data analysis, research and teaching.

Data management plan for the grant, "Collaborative Research: SaTC: CORE: Small: Privacy protection of Vehicles location in Spatial Crowdsourcing under realistic adversarial models." Research to develop new location privacy protection techniques by considering vehicles’ mobility features in the road network, and consequently lead to a more secure and trustworthy computing environment in location-based services (LBSs). As privacy concerns are still among the main obstacles for mobile users to participate in many advanced LBSs, this project is poised to contribute to the wider adoption of LBSs for many applications (e.g. navigation systems and location-based recommendation systems). The project will also provide a set of diverse and interesting topics for undergraduate and graduate students and outreach activities for the community.

Data management plan for the grant "The Influence of Aortic Valve Hemodynamics and LVAD on bio-transport processes in Calcific Aortic Valve Disease." One of the most common valvular heart diseases is the development of calcific aortic valve (CAV). The goal of this research is to understand the transport and near-wall accumulation of Low-Density Lipoprotein (LDL), which play a key role in the CAV development, with and without altered transvalvular pressure gradient due to LVADs. This will be achieved by developing the first computational model of biochemical transport near the highly deforming aortic valve, in which the hemodynamics and LDL mass transport are coupled with the biomechanical response of the valve, using a FSI framework formed around an innovative supreme immersed boundary (SIB) method.

Data management plan for the University of North Texas GenCyber Academy grant. The GenCyber Cybersecurity Program at the University of North Texas is part of the GenCyber program. The program is hosted by the Department of Computer Science and Engineering. The mission as part of the GenCyber program is to engage students at an early age in cybersecurity field and inspire them to become skilled cybersecurity professionals. This is provided by free summer cybersecurity camps for North Texas middle and high school students (7th-11th grade). The goals of the summer camps are to help students at an early age to understand correct and safe on-line behavior, increase students' interest in cybersecurity careers and improve diversity in the cybersecurity workforce of the nation.

Data management plan for the grant "Process and Materials Development for Additive Manufacturing (AM) of Tungsten-Based Alloys with Superior High Temperature Performance and Good Room Temperature Ductility." Research on CALPHAD simulations will be conducted to optimize the chemical composition of tungsten based alloys towards 3D printability while boosting mechanical performance through solid solution and dispersion strengthening. This will followed by powder prototyping to validate the simulation results experimentally and to finalize the chemical composition of interest. Process parameter optimization and post-process heat treatment design will help to achieve optimal low-to-high temperature mechanical properties. Mechanical testing and microstructural characterization will be performed to establish process-microstructure-property relationships.

Data management plan for the grant, "CAREER: Reinventing Network-on-Chips of GPU-Accelerated Systems." Research seeking to reinvent on-chip networks for GPU-accelerated systems to remove a communication bottleneck. A major outcome of the project is a set of techniques that enable the development of effective and efficient network-on-chip architectures. Graphics processing units (GPUs) have rapidly evolved to become high-performance accelerators for data-parallel computing. To fully take advantage of the computing power of GPUs, on-chip networks need to provide timely data movement to satisfy the requests of data by the processing cores. Currently, there exists a big gap between the fast-growing processing power of the GPU processing cores and the slow-increasing on-chip network bandwidth. Because of this, GPU-accelerated systems are interconnect-dominated and the on-chip network becomes their performance bottleneck.

Data management plan for the grant "IUCRC Planning Grant University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)." Research concentrating on interdisciplinary research, aiming to initiate and accelerate the transformation of mobility methods from conventional vehicles to electric, connected and autonomous vehicles by creating innovative electric, connected and autonomous technologies. The grant will create the Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT). A partnership between Wayne State University (WSU), University of North Texas (UNT), and Clarkson University (Clarkson), the center not only serves as an apparatus of academic researchers collaborating with industry on important problems, but also provides industry partners opportunities to access advanced synergic research produced from a diverse group of researchers.

Data management plan for the grant, "Ultrawideband Near-Field Probe System for Antenna Research." The objective of this project is to acquire an ultrawideband near-field probe system to build a highly capable antenna measurement facility which will provide critical support for the research, development, and testing of new antenna designs and concepts at the University of North Texas (UNT). The requested near-field probe system will be integrated with existing hardware at UNT, including a vector network analyzer and an Albatross Projects shielded chamber, to complete the build for the measurement system that is capable of characterizing three-dimensional radiation patterns of antennas-under-test (AUT) over a wide frequency range of 0.65-18 GHz. The measurement facility will be used to not only support ongoing and future federally funded research of the principal investigators but also enhance undergraduate and graduate education and training in RF, microwave, and antenna engineering at UNT.

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, "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, "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, "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.