Our client, Trailspur Capital Partners, is a real estate investment company based in Texas. We assist the company by building a database about commercial / industrial real estate to manage the market more easily and better decision-making. The business requires both the Geographic data from the County’s officials and the properties listings with vacancies information. Our goal is to design a database that can handle the aggregate data coming from both sources, which includes arranging and categorizing the properties, coming with several built-in functions namely identifying listings / vacancy changes, before deploying everything to the server. Our frontend, built with Vite and Vue, provides a smooth and interactive user experience while on the backend, we utilize AWS Lambda with Python to automate essential tasks, including downloading official county appraisal data, performing spatial merges using GIS functions, and managing our Supabase database. After successfully aggregating real estate data from both sources into a structured database, which enables easier tracking of property status changes, the platform efficiently processes and visualizes real-time property listings, allowing our client to analyze market trends and make data-driven investment decisions. Our project enhances real estate market intelligence for Trailspur Capital Partners. The system’s automated functions minimize manual workload and improve the accuracy of property tracking, providing a scalable solution for future expansion.

Cognitive Behavioral Therapy often relies on patients consistently completing therapeutic homework, regularly assigned by their therapist. A leading cause of Cognitive Behavioral Therapy failure for patients is non-compliance with their assigned therapeutic homework. About 20%-50% of patients fail to complete assignments due to inconvenience, a lack of clear instructions, or forgetting to finish the assignment. MENDmate is an online platform designed to solve this problem by providing a streamlined user experience for homework assignment and completion. MENDmate allows providers to assign homework to their patients and monitor their progress. It also provides patients with the ability to track and complete their homework assignments. Additional features of MENDmate include a learning library that gives patients an opportunity to learn about mental illness and practice coping techniques, a journal that allows patients to record their experiences and daily mood and anxiety assessments. MENDmate also tracks and reports the patient's data trends such as completed assignment, mood level, and anxiety level, which allows both the therapist and the patient to keep track of their progress over time.

Fort Worth PsychWorks, a leading psychiatry office, provides comprehensive neuropsychological and psychological assessments for patients across all age groups. Currently, after administering a variety of cognitive and behavioral tests, psychiatrists must manually input the resulting data into report templates, a process that is both labor-intensive and inefficient. This manual approach can take between 45 minutes to two hours per report, detracting from the time available for direct patient care and reducing the clinic’s overall operational efficiency.
To address this challenge, our senior design project introduces an automated report generation system named the PsychWorks Report Generation System. This software solution empowers psychiatrists to select or customize templates tailored to individual patient needs, add or remove specific tests, and automatically generate detailed report text, tables, and charts based on input scores. By maintaining the flexibility of the existing Excel-based system while significantly reducing the time and effort required for report creation, the PsychReport Automator enhances the user experience for clinic staff, boosts the potential for billable hours, and ultimately supports improved patient outcomes.

Obesity disproportionately affects underserved communities due to systemic barriers such as limited healthcare access, socioeconomic challenges, and a lack of culturally relevant health resources. Under the leadership of Dr. Christina Robinson and her team of medical students—Rumaila Hussain, Kavita Patel, Joice Song, and Fatema Jafferji—we are developing a mobile health application designed to support individuals in managing their health more effectively. This app will provide users with tools to track biometrics, manage chronic conditions, and set SMART (Specific, Measurable, Achievable, Relevant, Time-bound) health goals. A key feature of the app is a motivational text messaging system that encourages users to stay engaged with their health objectives. By integrating personalized and accessible interventions, this project aims to bridge healthcare gaps and empower individuals to take proactive steps toward healthier lifestyles.

The rapid advancement of artificial intelligence (AI) presents a unique opportunity to revolutionize education through personalized learning experiences. Traditional teaching methods often fail to address the diverse learning needs of students. This research explores the application of AI in education, focusing on machine learning algorithms, intelligent tutoring systems, and adaptive learning models to create personalized educational experiences. By analyzing student data, AI can optimize learning pathways, improve comprehension, and enhance engagement. The study discusses the potential, challenges, and future directions of AI-driven personalized education.

As global energy demand evolves, maintaining power quality has become a critical challenge in modern electrical systems. This research project examines key factors influencing power quality, focusing on maintaining a stable voltage magnitude and frequency across the grid. To achieve this, we explore techniques such as power factor correction and its role in improving energy efficiency and reducing costs. With the increasing integration of electric vehicles, data centers, and other high-power loads, new challenges arise in grid stability and demand management. Additionally, we investigate system overloading and transmission line considerations, addressing the risks of rising power demand and strategies for mitigating losses. Through this comprehensive study, we highlight the importance of power quality in ensuring the efficiency, reliability, and resilience of modern electrical infrastructures.

This project focuses on automating and standardizing the crowning process of a 15-foot Farnham roll form machine, used to shape aluminum parts, including fuselage and wing skins. The current crowning adjustment compensates for force imbalances caused by screws positioned at the machine’s ends and requires extensive manual shimming for optimal contact along 18 adjustable brackets. This process is detrimental to the manufacturing flow, as the time it takes to adjust the Farnham Press for different types of sheet metal or bends is long enough to significantly slow down production. To streamline this process, the project’s objectives are to design a method to measure bracket-to-material contact accurately, create an adjustable bracket system without the need for shims, and provide operators with real-time measurement feedback to optimize crowning adjustments efficiently. This will be achieved by redesigning the brackets with integrated sensors to accurately read the changing force along the beam.

Progress to date includes multiple bracket designs developed by the mechanical team, featuring adjustable mechanisms such as vertical screws, wedges, and easily insertable shims for depth control. Concurrently, the electrical team has conducted extensive research into sensor options and collaborated with sensor companies to identify suitable measurement solutions. Efforts are also underway to establish a data display interface that can provide real-time readouts from all 18 sensors, enabling operators to make informed adjustments during operation. Future work aims to explore a CNC-style interface for full control automation, which would allow streamlined adjustments for different part profiles and material thicknesses. This approach is expected to significantly reduce setup time and improve consistency in part quality.

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 imported into NASTRAN for further structural and acoustic analyses.

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.

The engineered concrete slab is a fundamental structure in construction with its mechanical properties influenced by the rebar placement, curing process, and the ratios of its primary components aggregate, cement, and sand. This study investigates how variations in rebar placement, concrete composition and curing methods effect the flexural strength of the sample. In ENGR 30014, 18 engineering teams produced their best sample of concrete with different ratios, rebar patterns, and different types of curing. The results provide insights into optimizing the concrete ratios, rebar placement, and methods for curing and their effect on flexural strength.

The goal of this research was to enable information transmission through light using a Phase Light Modulation (PLM) module to decode and display encrypted information. We conducted a literature review and set up an evaluation module capable of sending encrypted messages and transmitting data without the need for optical cables. Our setup includes a laser light source, a beam expander, a Digital Micromirror Device (DMD) controlled by an electronic control board, and a laptop running the software GUI provided by Texas Instruments. We conducted various experiments with these components to optimize the design and explore potential applications. Our findings highlight the potential of this technology for future data transmission and optical devices.