As industrial operations move to the state, residents find that their drinking water has been promised to companies. In 2019, Corpus Christi, Texas's eighth-largest city, moved forward with plans to build a desalination plant. The facility, which was expected to be completed by 2023, at a cost of a hundred and forty million dollars, would convert seawater into fresh water to be used by the area's many refineries and chemical plants. The former mayor called it "a pretty significant day in the life of our city." In anticipation of the plant's opening, the city committed to provide tens of millions of gallons of water per day to new industrial operations, including a plastics plant co-owned by ExxonMobil and the Saudi Basic Industries Corporation, a lithium refinery for Tesla batteries, and a "specialty chemicals" plant operated by Chemours.

Storm surge forecasting plays a crucial role in coastal disaster preparedness, yet existing machine learning approaches often suffer from limited spatial resolution, reliance on coastal station data, and poor generalization. Moreover, many prior models operate directly on unstructured spatial data, making them incompatible with modern deep learning architectures. In this work, we introduce a novel approach that projects unstructured water elevation fields onto structured Red Green Blue (RGB)-encoded image representations, enabling the application of Convolutional Long Short Term Memory (ConvLSTM) networks for end-to-end spatiotemporal surge forecasting. Our model further integrates ground-truth wind fields as dynamic conditioning signals and topo-bathymetry as a static input, capturing physically meaningful drivers of surge evolution. Evaluated on a large-scale dataset of synthetic storms in the Gulf of Mexico, our method demonstrates robust 48-hour forecasting performance across multiple regions along the Texas coast and exhibits strong spatial extensibility to other coastal areas. By combining structured representation, physically grounded forcings, and scalable deep learning, this study advances the frontier of storm surge forecasting in usability, adaptability, and interpretability.

The intersection of LLMs (Large Language Models) and UAV (Unoccupied Aerial Vehicles) technology represents a promising field of research with the potential to enhance UAV capabilities significantly. This study explores the application of LLMs in UAV control, focusing on the opportunities for integrating advanced natural language processing into autonomous aerial systems. By enabling UAVs to interpret and respond to natural language commands, LLMs simplify the UAV control and usage, making them accessible to a broader user base and facilitating more intuitive human-machine interactions. The paper discusses several key areas where LLMs can impact UAV technology, including autonomous decision-making, dynamic mission planning, enhanced situational awareness, and improved safety protocols. Through a comprehensive review of current developments and potential future directions, this study aims to highlight how LLMs can transform UAV operations, making them more adaptable, responsive, and efficient in complex environments. A template development framework for integrating LLMs in UAV control is also described. Proof of Concept results that integrate existing LLM models and popular robotic simulation platforms are demonstrated. The findings suggest that while there are substantial technical and ethical challenges to address, integrating LLMs into UAV control holds promising implications for advancing autonomous aerial systems.

Fifty years ago, Mike Sanders watched with awe and anticipation as the crew of Apollo 11--Neil Armstrong, Buzz Aldrin, and Michael Collins--splashed down in the Pacific Ocean. Landing men on the moon and returning them safely to the earth was a seminal moment in the history of flight, and it had a profound effect on then 7-year-old Sanders, who now heads the Lone Star UAS Center of Excellence & Innovation at Texas A&M University–Corpus Christi. Looking back, Sanders says he never expected the day to come when he would be working with NASA on anything, let alone another chapter in the history of flight. But this year, he landed in the middle of one of the most important aeronautical projects of this generation: an effort to build a safe and effective unmanned aircraft system traffic management (UTM) platform. In August, Texas A&M–Corpus Christi's Lone Star UAS Center of Excellence and its partners' workers stood alongside NASA scientists and engineers as they flew 22 small physical and digital drones above and between tall buildings in five areas of Corpus Christi. The low-altitude test culminated a five-year effort to learn what it would take to build a nationwide system for managing low-altitude drone traffic.

NASA is ready to put its drone traffic management system to the ultimate test and has chosen Nevada and Texas as its final testing sites. The agency, together with the FAA, has been developing an Unmanned aircraft Traffic Management (UTM) system over the past four years in an effort to figure out how to safely fly drones in an urban environment. Now that the project is in its last phase, it has teamed up with the Nevada Institute for Autonomous Systems in Las Vegas and the Lone Star UAS Center for Excellence & Innovation in Corpus Christi, Texas to conduct a final series of technical demonstrations. NASA and the FAA are planning to demo a big list of technologies, including their interface with vehicle-integrated detect-and-avoid capabilities, vehicle-to-vehicle communication and collision avoidance, as well as automated safe landing technologies. All those will help NASA understand the challenges of flying in an urban environment and conjure up ideas for future rules and policies.