Data management plan for the grant, "REU Site: TaMaLe - Testing and Machine Learning for Context-Driven Systems: Research Experience for Undergraduates." TaMaLe (Testing and Machine Learning for Context-Driven Systems), a renewal Research Experience for Undergraduates (REU) Site at University of North Texas, engages 10 undergraduate students for 10 weeks with problems in the context-driven system domain. The students explore research problems to improve the reliability and security of context-driven systems. Context-driven systems, such as mobile apps, face constant streams of input from both users and context changes in their environments. Users interact with apps through touch and speech interfaces. These systems also respond to context events that occur in their environments such as changes to network connection, battery level, screen orientation, and more. The combined explosion of possible user events and context event sequences pose new challenges that require cost effective testing solutions. Students and mentors in this REU program work in small teams to develop and empirically evaluate new software testing techniques for context-driven systems using strategies such as reinforcement learning and combinatorial-based techniques.

Article investigates the strength-ductility synergy of high-entropy alloys (HEAs).

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, "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, "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."

Data management plan for the grant "Collaborative Research: Microneedle‐mediated Adaptive Phototherapy (MAP) for Wound Healing."

Data management plan for the grant "Collaborative Research: Smart Stent for Post-Endovascular Aneurysm Repair." The overall objective of this proposal is to develop a Smart Stent for post-endovascular aneurysm repair (EVAR) surveillance that combines a flexible, hybrid, seamless, battery-less bioelectronic system with a deep-learning algorithm that offers an automated diagnosis of complications such as endoleak.

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, "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.