Agent-based models (ABMs) are widely used in computational biology to simulate complex processes such as infectious disease transmission. However, many research-grade models are implemented primarily as backend systems and lack graphical interfaces that allow researchers to efficiently configure simulations and interpret outputs. In collaboration with the Biophysics Department, this project focused on the development of a graphical user interface (GUI) for an existing viral agent-based simulation platform previously implemented without an interactive frontend.
The implemented interface integrates with the existing backend simulation environment deployed on laboratory systems, enabling structured parameter configuration, simulation execution, and visualization of model outputs. Development focused on frontend architecture, parameter validation mechanisms, backend connectivity, and data visualization components for simulation result analysis. Additional work included interface refactoring and codebase cleanup to improve maintainability and usability.
The resulting system provides a structured workflow for configuring and executing simulations while preventing invalid parameter configurations through input validation. By extending the existing modeling framework with a robust graphical interface and visualization capabilities, this work improves accessibility and operational efficiency for researchers conducting computational epidemiology experiments within the laboratory environment.

Machine Performance Check Plus (MPC+) is a software platform designed to improve quality assurance workflows for Varian TrueBeam linear accelerators used in radiation therapy. The system automatically collects and processes Machine Performance Check (MPC) data generated by clinical machines, including imaging files and measurement results, and converts them into structured, analyzable information. The platform provides a web-based dashboard that allows medical physicists and clinical staff to review machine performance metrics, visualize trends, and quickly identify values that fall outside acceptable tolerances. MPC+ also supports digital sign-off workflow and audit trails to ensure compliance with regulatory and clinical standards. By consolidating data from multiple machines and clinics into a single interface, the system reduces the time required for daily QA review while improving reliability and traceability. Overall, the project aims to make the quality assurance process more efficient, data-driven, and scalable for radiation oncology clinics operating Varian TrueBeam systems.

Large language models (LLMs) such as ChatGPT, Claude, Gemini, and Llama are increasingly being deployed as search and decision-support tools for health-related inquiries. As users provide demographic context, including age, to obtain personalized guidance, these systems can differentially adjust tone, directive strength, or safety framework. Although age can be clinically relevant, unintended variation in the generated advice can introduce systematic safety disparities or representational bias. In this study, we analyze outputs from two major LLM families across 10,679 physical and mental health scenarios to examine how explicit age cues, including teen, young adult, middle-aged, and senior, influence the safety and linguistic properties of generated health advice. To quantify these effects, we introduce three task-specific evaluation metrics: Age Differential Safety Bias (ADSB) to measure relative safety shifts under demographic conditioning, Safety Risk Score (SRS) to capture cumulative weighted unsafe recommendations, and Tone Differential Index (TDI) to detect systematic changes in linguistic complexity and formality associated with representational harm. The results indicate that explicit age cues systematically alter the behavior of the model. Demographic conditioning consistently reduces safety quality relative to age-neutral baselines. Middle-aged cohorts exhibit a higher cumulative safety risk in directive responses, whereas senior cohorts demonstrate elevated tone shifts consistent with oversimplification and increased formality. These findings suggest that demographic sensitivity can introduce measurable allocative and representational disparities in healthcare-oriented LLM systems. This work establishes a reproducible audit framework for evaluating demographic safety sensitivity in health-focused LLM deployments and contributes to the development of standardized evaluation protocols for safer and more equitable integration of AI systems in clinical and consumer health environments.

The BatLab project aims to develop a machine learning based tool that assists researchers in identifying bat species from acoustic recordings. Bats rely on echolocation calls that vary in frequency, duration, and shape, allowing species to be distinguished through analysis of their recorded calls. Currently, researchers must manually review large volumes of acoustic recordings, which is a time consuming process that limits the scale of ecological studies. This project explores the use of supervised machine learning to automate the classification of bat echolocation calls using labeled training data. The system analyzes acoustic features within recorded calls and predicts the most likely species while flagging uncertain cases for further review. In addition, the project focuses on improving data organization and providing a user friendly interface that allows researchers to efficiently visualize and manage acoustic data. By reducing the manual workload involved in analyzing bat call recordings, the BatLab system aims to support ecological research and improve the efficiency of studying bat populations.

This senior design project focuses on developing PostAgent, an AI-powered content creation platform created by Corevation, an innovations tech company. This product is aimed at helping businesses and entrepreneurs with creating and managing social media content more efficiently and allow marketing endeavors to be more manageable. Our team is building multiple features, including AI tools to regenerate and edit post text and images, an analytics dashboard for tracking social media performance, and a centralized content library for organization purposes and for users to upload custom content to the platform. We are also assisting in the overall UI/UX to ensure an intuitive user experience and developing a company website to support Corevation’s public presence. Together, these components demonstrate a full-stack approach to product development, blending AI capabilities with user-centered design.

How would one classify an apple fruit versus an apple phone? Typically as a fruit and a technology device. However some modern systems for classifying patents are insufficent and would be unable to differentiate between the two and cluster both based on their containing the word ‘apple’. Our task with iPELiNT is to build upon solutions to better visualize how USPTO( United States Patent and Trademark Office) art unit’s change over time. An art unit is a group of USPTO examiners specializing in a specific technology area. Our end product helped establish a data-driven system for conducting forensic analysis of USPTO patent examiner dockets using vector embeddings and internal data pipelines. We used mongoDB for our database, JavaScript and Python for our backend, and NuxtJS and Vue for our frontend. Our 5 phases of development are as follows. 1. Data Aggregation and Preparation. 2. Centroid Calculation and Art Unit Profiling. 3. Deviation Analysis and Scoring 4. Visualization and interpretation Framework.

Artificial intelligence tools, especially large language models (LLMs) are progressively being integrated into educational settings as resources that can enhance student learning and offer novel methods for information retrieval. As these technologies advance, educators and researchers are increasingly focused in comprehending their impact on student learning and engagement with academic content. This study examines the potential role of AI-based systems in facilitating student learning by analyzing various ways employed by students to obtain and process information during study activities.
The study's participants are split up into four groups, each of which accesses learning resources in a different way. The first group relies on traditional text-based study resources. The second group uses general online resources to gather information. The third group is allowed to use AI-based tools powered by large language models to receive explanations and assistance. The fourth group uses a hybrid strategy that blends AI-supported tools with conventional study materials.
The performance and learning experiences of these groups are compared to evaluate how different resources influence students’ understanding of course concepts. The findings are expected to provide insight on whether AI technologies can successfully supplement conventional teaching methods. Understanding these effects help educators determine how to appropriately incorporate AI and LLM tools into classroom settings to improve learning while upholding efficient teaching methods.

The Driving Safety Certificate Management System is a web application designed to streamline
the administration of driving safety courses in Texas. Currently, instructors conduct classes
independently but rely on the licensed provider to process student information, retrieve driving
records, and issue course completion certificates, which can cause delays and create additional
administrative work. This system shifts those responsibilities directly to instructors by allowing
them to manage classes, enroll students, process student information, and generate certificates
through a centralized platform. By automating these processes, the system reduces manual
workload, improves efficiency, and enables faster certificate delivery for students. The
application also maintains oversight for administrators while ensuring that instructors can
operate more independently within the requirements set by the Texas Department of Licensing
and Regulation.

This study analyzed droplet sizes generated by nebulizers by collecting aerosolized liquid on microscope test slides and processing microscope images with MATLAB to quantify droplet distributions. Measurements were compared to the target droplet size range required for effective nebulization, since droplets outside this range can reduce respiratory delivery efficiency. Results help evaluate nebulizer performance and ensure droplets meet specifications for optimal aerosol behavior.

This project explores the design of a grid independent community in Fort Worth with 200 houses using only solar and wind energy sources. Data for the project has been obtained from Dr. Michaelides, which includes excel spreadsheets and research to aid in finding optimal efficiencies in the design of buildings. The design will include energy production, usage, and storage. We are planning on using one small wind turbine with supplementary solar power; we will also be able to store excess energy. We will do calculations to determine how much energy needs to be stored and how large our solar panels need to be to sustain our community.

Solar net-zero energy buildings (NZEBs) are energy-efficient structures that generate as much electricity on-site as they consume over one year. This project involves designing a net-zero solar home in Fort Worth, Texas, using well-insulated construction materials, optimized building orientation to maximize sunlight, and efficient heating and cooling equipment. The home’s energy demand is met primarily by electricity produced from a photovoltaic (PV) system, while space heating and cooling are provided by a ground source heat pump (GSHP). Energy calculations and modeling are performed to estimate annual electricity consumption, determine the required PV system size, and evaluate GSHP operation. Results indicate that the home can reach net-zero energy performance under typical climate conditions in Fort Worth. This project shows that combining on-site solar generation with energy-efficient design strategies can significantly reduce residential energy use and lower environmental impact.

An analysis of the sound-producing characteristics of a tenor trombone has been initiated at TCU. Focus of the effort will be on the model Conn 44H "Vocabell" tenor trombone due to its unique rimless bell. A numerical model of the instrument using Autodesk Inventor has been created. The model was then analyzed using COMSOL Multiphysics.

Key areas of focus include understanding the interaction between the instrument's structural vibrations and the sound radiated from the bell. The "Vocabell" design, known for its unique construction and acoustic qualities, will be critically examined to assess how its geometry and material properties influence sound production and associated frequency spectrum. Radiated sound and structural vibration measurements have been conducted on the physical instrument, providing data for model correlation and validation. Once validated, the numerical model will be used to explore more advanced concepts of brass instrument design.

Ultrasonic cavitation is a critical process in industrial cleaning and sonochemistry, yet its efficiency is frequently compromised by inconsistent energy distribution. This research investigates the complex inner workings of acoustic harmonics and their influence on the cavitation field within a contained tub. While traditional systems suffer from undesirable "hot spots" and "dead zones," this project systematically examines how transducer positioning and operating frequencies govern harmonic resonance and subsequent cavitation intensity. Utilizing a controlled test tub, empirical data will be gathered through standardized measurement techniques, including aluminum foil erosion, to visualize and map harmonic wave patterns. The primary objective is to quantify the relationship between these resonant frequencies and cavitation uniformity. The findings will yield actionable design principles for optimizing ultrasonic systems, advancing our understanding of harmonic behavior to improve process efficiency and consistency in precision engineering applications.

This paper discusses the responsibility of a 20 MW photovoltaic solar plant in Yuma, Arizona. It presents the requirements to create new power parameters using historical solar irradiance from 2018 to 2024 for Yuma, Arizona, to determine the efficiency, cost, and area requirements for PV solar cells, along with yearly plant performance over its operational lifetime. The hourly data included were wind speed, temperature, solar zenith angle, surface albedo, direct normal irradiance (DNI), diffuse horizontal irradiance (DHI), and global horizontal irradiance (GHI). This power plant design utilizes solar radiation data from the National Renewable Energy Laboratory's National Solar Radiation Database (NSRDB). The analysis investigates plant performance using temperature derating factors, capacity factor calculations (23.6%), and thermal modeling to ensure reliable 41.2 GWh annual output.

A team of TCU engineers has explored 3 different processes used for nebulizing medication: jet, mesh, & ultrasonic. The mesh nebulizer has successful been prototyped by the team and has been tested with various medications. The goal of this prototype is to nebulizer the patients medication more quickly, to make the delivery process more expedient & convenient.

This paper reports the design of an 80 MW Wind Farm in Marfa, TX. I have selected a group of the Siemens SWT-2.3-108 turbines for the moderate winds felt in Marfa (as compared to the Texas panhandle). A layout is developed to reduce wake losses and satisfy noise & environmental constraints. The point of interconnection for the farm is the ERCOT transmission system. Project economics are estimated from AEP, O&M, and federal tax incentives to gauge the electricity cost and overall viability.

This project proposes the design and implementation of a net-zero energy home in Fort Worth, Texas, powered primarily through solar energy. The objective is to offset all annual household electricity consumption through on-site renewable generation. Based on average yearly usage, the average home requires approximately 13128 kWh of electricity per year. To meet the demands, the system incorporates a solar photovoltaic (PV) array sized to generate a sufficient amount of power to offset the full electricity usage per year, sending energy back to the grid when the amount generated exceeds the demand, and supplementing energy from the grid when the demand is higher than the supply. The design accounts for seasonal variation in solar irradiance typical of North Texas. This model demonstrates the feasibility of sustainable residential energy independence in the Fort Worth region.

Abstract: This paper will detail the general design and operation of an 80-MW wind power plant in the high wind Carbon County of Southeast Wyoming. Using hourly wind data, the study will model wind profiles and perform parametric analyses of different design aspects. The research will discuss two possible configurations of 22 x 4 MW turbines or 30 x 3 MW turbines to determine the impact that an individual turbine’s capacity can have on design and energy production/efficiency. The study will focus on analyzing the Annual Energy Production and Capacity Factor based on height and spacing. The goal of this study is to design the better of the two configurations after analyzing them to determine which will provide a better energy output.

A net zero energy house is a residential dwelling that produces an amount of electric energy that is at least equivalent to the amount of electric energy it consumes. This report describes technology and methods applicable to the creation of a net zero electric energy consumption house in Phoenix, Arizona with a focus on energy from photovoltaic sources. Specifically, this report utilizes local environmental and energy usage data to prescribe an appropriately sized solar energy system combined with energy saving insulation practices to reduce power grid draw while maintaining modern conveniences.

This project proposes the design of a self-sustaining, solar-powered golf course and resort
development in Fort Worth, Texas. The development will include an 18-hole course, a clubhouse,
and 40 villas (4 occupants per unit), all powered by an on-site photovoltaic solar farm integrated
with a battery energy storage system. Annual energy demand will be estimated using published
golf course energy data from the Golf Course Superintendents Association of America (GCSAA
Phase II Energy Survey) and residential electricity consumption data for Texas. Total system
wattage (W), annual energy use (kWh/yr), peak demand (kW), and storage capacity (kWh) will
be calculated using standard methods from Energy, the Environment, and Sustainability. Data to
be collected includes the median annual golf course energy consumption, irrigation pumping
requirements, clubhouse loads, per-capita residential electricity use, average solar irradiance in
North Texas, photovoltaic module efficiency, system losses, and battery round-trip efficiency.
Water supply options will be evaluated using regional sources and potential on-site groundwater
or reclaimed water strategies, with associated pumping energy incorporated into total load
calculations. Primary calculations will determine the required photovoltaic capacity, the land
area for the solar farm, storage sizing for overnight and low-irradiance periods, and the overall
system efficiency. Anticipated results include demonstrating the technical feasibility of a
net-zero-energy golf course development in Fort Worth, estimating total installed capacity in
megawatts, and quantifying reductions in grid dependence and operational carbon emissions
compared to conventional golf course operation

The TaskTimer project focuses on the development of an automated task management board designed to support individuals living with dementia and other memory-related conditions. People with dementia often benefit from consistent routines and clear visual reminders, but traditional task boards require manual resets and caregiver supervision. The TaskTimer addresses this challenge by providing an electronic system that displays daily tasks, allows users to easily mark them as complete, and automatically resets tasks at midnight. The system uses an embedded computing module connected to a display to present tasks in a clear and simple interface. When a task is completed, the next action moves into view, helping users stay focused on what needs to be done next. Tasks can also be scheduled to appear on specific days or exist for only one day, allowing routines to be tailored to individual needs. In addition, a caregiver application was developed to allow caregivers to remotely add or modify tasks, monitor whether tasks have been completed, and manage the user’s schedule. By combining an accessible task display with remote monitoring capabilities, the TaskTimer helps individuals with dementia maintain daily routines while reducing the level of supervision required from caregivers.

Abstract

The development of large-scale photovoltaic systems in high-irradiance regions can significantly support the decarbonization of the electricity generation industry. This project presents the design of a 20-MW grid-connected photovoltaic power plant in Ouarzazate, Morocco. Hourly solar irradiance data from 2017 to 2019 were used to compute plane-of-array radiation and the resulting power output with a module nominal efficiency of 22%. The required panel area was determined from rated conditions, and the annual energy production was calculated by summing the hourly energy generation. Results indicate an annual electricity generation of approximately 55.9 GWh with inter-annual variation below 5%.

This project presents a preliminary design for a 20 MW thermal biomass power plant in southern Louisiana. The proposed plant will use a blended biomass fuel stream based on regional availability, including rice hulls, bagasse, and switchgrass/wood chips (planned 30/30/40 mixture, with final basis and assumptions to be justified). The analysis will use standard thermodynamic notation and methods from class and the textbook.
A Rankine cycle model will be used to estimate the plant thermal energy requirement and determine the annual energy demand (MJ/yr) needed to maintain the target electrical output. Using lower heating value (LHV) data from biomass property tables, the study will then calculate the required annual biomass consumption (kg/yr), including the mass of each biomass type in the proposed blend. In addition to the energy balance, the project will evaluate biomass transportation logistics by estimating the number of truckloads required per year and the land area needed to support switchgrass production within the regional agricultural system.

This poster presents the structural design of the Automated Parts Washer (APW), a senior capstone sponsored by Mary Kay and developed by engineering students at Texas Christian University. The APW is designed to provide an automated ultrasonic cleaning solution for cosmetic manufacturing components such as nozzles, caps, and trays. All structural components of the system have been modeled in Autodesk Inventor to enable a fully integrated digital design environment that supports visualization, dimensional coordination, and verification of system layout prior to fabrication. The washer frame utilizes 80/20 aluminum structural members, selected for their strength, modularity, and ease of assembly. This material choice provides flexibility in frame configuration, allowing rapid design iteration and future modification while maintaining robust structural support for the fluid-filled wash tank, ultrasonic hardware, and plumbing systems. The resulting design balances structural integrity, manufacturability, and adaptability for prototype construction and testing.

This paper presents the design and electrical performance analysis of a 10-MW
grid-connected photovoltaic (PV) power plant located west of Fort Worth, Texas, in a region selected for high solar irradiance, flat terrain, and transmission accessibility. Emphasis is placed on electrical system architecture, including module configuration,
DC string sizing, inverter selection, transformer integration, and interconnection with the utility grid. A single-axis tracking (panel pivoting) system is incorporated to maximize incident solar radiation and increase daily energy capture. Parametric studies
are performed on tilt angle, tracking strategy, module efficiency, and inverter performance to evaluate their influence on overall system output and electrical efficiency.