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 "Travel: NSF Student Travel Grant for 2023 Great Lakes Bioinformatics Conference (GLBIO)"

Data management plan for the grant "IUCRC Phase I University of North Texas: Center for Electric, Connected and Autonomous Technologies for Mobility (eCAT)."

Data management plan for the grant "Self -powered Wireless Hybrid Density/Level Sensing with Differential Pressure Sensors for Safeguarding and Monitoring of Electrochemical Processing of Nuclear Spent Fuel."

Data management plan for the grant "Consortium on Sensing, Energy-efficient Electronics and Photonics with 2D Materials and Integrated Systems for Training the Next-Generation"

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, "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, "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, "Development of Physics-Informed Mathematical Models for Ship Extreme Responses in Ocean Waves."

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, "Optic-nerve-head (ONH) Chips for Glaucomatous Neurodegeneration." The most prominent causative risk factor of glaucoma, the leading cause of irreversible blindness worldwide, is elevated intraocular pressure (IOP), which could deform the optic nerve head (ONH) and cause glaucomatous neurodegeneration. However, current glaucoma therapies that focus on lowering IOP do not stop vision loss effectively, and thus there is a pressing need to understand the mechanisms underlying glaucoma pathogenesis. In this project, we will develop a biomimetic 3-D ONH-on-a-chip system that closely mimics the key anatomical and pathophysiological characteristics of the native ONH to study astrocytic mechanisms of glaucoma pathogenesis, a missing link to develop efficacious therapies.

Data management plan for the grant, "Child Health and Human Development Extramural Research." This study will use the genome-edited human induced pluripotent stem cell (hiPSC) with NOTCH1 knockout to recapitulate the genetic variants of NOTCH1 mutation in the Hypoplastic left heart syndrome (HLHS). It will use advances in the vascularized cardiac organoid directly differentiated from hiPSCs to replay the development and function of cardiomyocytes, endothelial cells, smooth muscle cells, and other cardiac cells in a defined 3D cell culture model by stencil-based micropatterning. It will elucidate the pathogenesis of cardiovascular underdevelopment and dysfunction found in HLHS with NOTCH1 mutation via the NOTCHDELTA/JAG ligand-receptor binding and multicellular crosstalk by single-cell RNA-seq and proteomics analysis.

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, "A Novel Hybrid-Mode Curved-shape SAW (CsSAW) Sensor For The In-Situ/RealTime Torque And Temperature Measurement of Rotor Shaft in Harsh Environments."

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, "The University of North Texas Industrial Assessment Center."

Article provides an overview of recent progress in superlubricity research involving solid, liquid, and gaseous media and discuss the prospects for achieving superlubricity in engineering applications leading to greater efficiency, durability, environmental quality, and hence global sustainability.

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.

Article exploring graph node property prediction. Originally presented as part of the application track at the 38th International Conference on Logic Programming in Haifa, Israel.

This article proposes a novel flower classification method that combines enhanced VGG16 (E-VGG16) with decision fusion.

Data management plan for the grant, "Cancer Detection and Treatment Based on Low-Level Light Interaction with Biological Cells: Augmentation to Existing Human Performance Research Capabilities."

Data management plan for the grant, "EAGER: Experimental Methods and Measurements of Anomalous Properties of Supercritical Fluids and their Mixtures"

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

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.