Congratulations to the funded 2024 Texas A&M Engineering Experiment Station (TEES) research collaborations.
$10,000 Award
BANDs
Project Title: Advancing Neurodegenerative Disease (ND) Management: Sensor-Based Risk Profiling and Disease Progression Monitoring
Team Members: Principal Investigator Ngozi Mbue, Texas Woman’s University-Houston; Rhett Rigby, Texas Woman’s University-Denton; Karen N. Williams, Del Mar College; Fatemeh (Azi) Tabei, West Texas A&M University
Neurodegenerative diseases affect 7 million individuals in the U.S. Challenges exist with characterizing the risk factors and pathophysiology from the initial symptom appearance through the eventual diagnosis of these diseases. Our solution is in-sole wearable sensors that combine a risk factor profile (i.e., physical activity habits, sleep quality and duration, cytokine concentrations) and progression (i.e., dyskinesia, tremors, rigidity, gait abnormalities) of neurodegenerative diseases including Parkinson’s disease and Alzheimer’s disease. The uniqueness of these sensors stems from the combination of these measures, as well as novel sensors that can measure pro-inflammatory biomarkers through sweat and muscle rigidity with integrated strain gauges. These sensors will be lightweight and cost-effective, as well as objective, accurate, and reliable. Data can be synced wirelessly to a customized dashboard app tailored to the client for physicians’ review. With our team’s background in sensor technology, neurodegenerative disease, biomechanics, and data mining, we will be successful—viable funding sources including public (i.e., NIH, NSF) and private grants.
$7,500 Award
Realistic Sustainable Solution
Project Title: Sustainability and life-cycle analysis for recycling materials in construction
Team members: Principal Investigator Jianxin Huang, Texas A&M Engineering Experiment Station; Ayman Elzohairy, Texas A&M University-Commerce; Euijin Yang, Sam Houston State University; Jianxin Huang, Texas A&M Engineering Experiment Station; Andrew Sorensen, Texas A&M University
As the construction industry increasingly turns to sustainable practices, the use of recycled materials in concrete for highway construction has received significant attention. However, there has been a lack of attention on the end-of-service life viability and the environmental impacts of such recycled materials. This project will focus on identifying the type of contaminants from the recycled concrete from in highway constructions that get released to the environment, quantifying the level of contaminants, and evaluating the suitability for future recycling. This will be achieved by collecting core samples from existing infrastructure (i.e., highway) that have reached their service life and conducting multiple tests to identify the pollutants captured during their life cycle. The tests include material and chemical analysis, such as X-ray Diffraction (XRD), leaching test, and Fourier Transform Infrared Spectroscopy (FTIR). Based on the test results, we can evaluate the risks of contaminants and suitability for reuse of the materials.
$5,000 Award
SAFE: Shear thickening Adaptable Foams for Elderly
Project Title: Wearable shear thickening composite scafolds for today’s biomedical applications
Team members: Principal Investigator Abolghassem Zabihollah, Tarleton State University; Pavan V Kolluru, Texas A&M University; Sushil Doranga, Lamar University; Mohammad Naraghi, Texas A&M University; Yunxiang Gao; Prairie View A&M University; Sayantan Das, Texas A&M University-San Antonio; Harini Gunda, Texas A&M Engineering Experiment Station
Sideways falling is a very common incident in elderly which might occur due to several reasons, including, lose in stability, paralysis, or other underlying conditions. For example, twisting an ankle is a common way of losing individual balance which may end up with falling and sprained (twisting) ankle injury. The consequences of these fractures range from impaired mobility, increased rehabilitation costs, and escalated hospitalization to increased risk of fatality. It is estimated that 75% of people who experience a falls-related hip fracture are wearing inappropriate footwear. Our Solution: This project aims to develop comfortable, yet protective wearable devices, with passive stiffness control, that support bodily parts, such as hip, ankle and shoulder, during undesired and sudden twisting and loss of balance. The main novelty in this work is the utilization of shear thickening materials (STM) which are embedded inside porous media, acting as the individualized shape-customizable container of the STM.
SpaceH2 Station
Project Title: Feasibility of H2 Generation on the Moon
Team members: Principal Investigator Kiseok Kim, Texas A&M University; Emmanuel Dada, Prairie View A&M University; Swagnik Guhathakurta, Texas A&M University; Hongbo Du, Tarleton State University; Hoe-Gil Lee, Tarleton State University; Rita Okoroafor, Texas A&M University
The project aims to explore hydrogen generation from space rock through serpentinization to determine if generating hydrogen in space can save travel time and energy in traversing from Earth to Mars. This project will combine experiments, energy efficiency studies, techno-economic analysis, life cycle assessments, and water sustainability studies to evaluate the feasibility. The goal is to achieve a cost <$4/kgH2, <8kgCO2/KgH2 equivalent, >33% energy conversion efficiency. If successful, this would provide a means for refueling, thereby saving costs and energy associated with earth-to-moon travel.
Intelligent Water Security (IWS)
Project Title: Intelligent early warning framework for water resource safety using autonomous UAV swarm
Team members: Principal Investigator Soyoon Kum, Angelo State University; Amirali Najafi, Texas A&M University; Ning Luo, Texas A&M University-Corpus Christi; Ramzi Taha, Texas A&M University; Mansour Karkoub, Lamar University
Following natural and human-induced disasters, such as hurricanes, water resources must be tested to ensure public safety and health, as this impacts drinking water and other food chain. The current state of practice is to deploy water safety inspectors to sample and test the water resources. However, this process is time-consuming and tedious and may be impeded by accessibility issues. We propose employing robotics and artificial intelligence to rapidly and autonomously monitor and detect contaminants over large areas quickly and autonomously. This will involve deploying an autonomous swarm of UAVs with onboard sensors, cameras, and sampling devices.
$2,500 Award
Cyber Griffin
Project Title: Trustworthy Cyber Intelligence
Team members: Principal Investigator Erdogan Dogdu, Angelo State University; Srujan Kotikela, Texas A&M University-Commerce; Avdesh Mishra, Texas A&M University-Kingsville; Lucy Tsado, Lamar University; Yongzhi Wang, Texas A&M University-Corpus Christi; Garth Crosby, Texas A&M University
A typical cyber analyst has to deal with hundreds of security alerts every day. Analyzing and resolving these alerts in a timely manner is not humanly sustainable. The situation is exacerbated with the shortage of cyber professionals and increasing cyber attacks. Existing AI solutions used for automation are either unreliable or poorly performing. We propose a knowledge-based AI framework that provides automation and remediation for network security. Our framework involves both hardware and software techniques to ensure the security of AI is trustworthy. We will partner with TEEX to develop a curriculum for workforce development.
NEUROVIVO
Project Title: Environmental Exposure-Based Early Parkinson’s Diagnosis: A High-Throughput Approach Using In Vivo Drosophila Models and Human Neuronal Models
Team members: Principal Investigator James Henry, Lamar University; Greg Reeves, Texas A&M University; Jabia Chowdhury, Texas A&M University-Texarkana
Parkinson’s disease (PD) is the second most common neurodegenerative disease behind Alzheimer’s, totaling almost a million people living with the disease in the US. Currently, there is no definitive test for PD, and at the time of diagnosis, the disease has already significantly progressed to physical symptoms and significantly impact patient quality of life. In this project, team Neurovivo is proposing to identify clear biomolecular markers for early detection and to develop a high-throughput screening process for additional environmental triggers. While several cellular processes are known to be involved in the progression of the disease, including the shape and function of mitochondria, a quantitative understanding of disease progression is currently lacking. To bridge this gap, we will use a combination of proven in vivo and cell culture models of PD (Drosophila and human cell line SHSY-5Y, respectively) to quantify the disease progression in live cells. We will perform live cell imaging of healthy cells, genetic mutants, and cells exposed to known environmental effectors, and develop and validate both classic mathematical models and machine learning algorithms to identify features of disease progression. The validated models will then be used as a tool for high throughput screening of novel disease-causing environmental factors in our two biological systems. The ultimate result will be an accessible, simple screening platform for identification of environmental triggers and early diagnostic tools for improved patient outcomes.
EVAC (Electric Vehicle Active Coordination)
Project Title: Intelligent EV Charging Coordination During Natural Disasters for Grid Resiliency
Team members: Principal Investigator Antonio Medrano, Texas A&M University-Corpus Christi; Jangwoon Park, Texas A&M University-Corpus Christi; Lianxiang Yun, Texas A&M University-Corpus Christi; Anwarul Sifat, Lamar University; Guanyu Tian; Texas A&M University-Galveston; Xin Chen, Texas A&M University
Electric vehicle (EV) owners face unique challenges during natural disasters to maintain full mobility. This project will develop an intelligent system to provide EV owners with coordinated information on where to charge and navigate to evacuate while maintaining grid resiliency.
