UGROW25 Projects

Mentor: Dr. Liang 

Email: fucheng.liang@msutexas.edu

Department: Chemistry 

Project Title: Developing a Fluorescence Resonance Energy Transfer (FRET) Assay to Study the Disaggregation Activity of a Membrane Protein Chaperone 

Keywords: Alzheimer’s disease, protein aggregation, Tau, amyloid beta, protein misfolding 

Project Description:  

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and the accumulation of misfolded Tau proteins, yet its precise pathological mechanisms remain unclear. Tau, an intrinsically disordered protein that binds microtubules, adopts a cross-beta sheet structure when forming neurofibrillary tangles in AD. According to the amyloid hypothesis, Tau aggregation drives AD pathology, making its clearance a promising therapeutic target. The chloroplast signal recognition particle 43 (cpSRP43), a membrane protein chaperone in plant chloroplasts, has been identified as a unique model for preventing and reversing protein aggregation. Unlike conventional chaperones, cpSRP43 disaggregates proteins without ATP, suggesting its potential as a novel therapeutic agent. This study aims to investigate whether cpSRP43 can prevent and reverse Tau aggregation. 

Aim: Assess the impact of cpSRP43 on Tau aggregation. 

Objective: Determine whether cpSRP43 can disrupt Tau aggregation. 

Hypothesis: cpSRP43 prevents and disrupts Tau aggregation. 

Activities:  Preliminary data suggest that cpSRP43 utilizes its binding energy to prevent and disrupt amyloid aggregation. To test this hypothesis, we will: 

(1) Overexpress and purify Tau using bacterial expression systems, followed by affinity and ion-exchange chromatography. 

(2) Develop a FRET assay to assess the disaggregation activity of cpSRP43. 

Anticipated Outcomes: We expect that cpSRP43 will prevent and disrupt Tau aggregation, providing insights into potential therapeutic strategies for AD. 

Mentor: Dr. Colmenares-Diaz 

Email: eduardo.colmenares@msutexas.edu

Department: Computer Science 

Project Title: A First year Introduction to High Performance Computing 

Keywords: Parallel, CPU, GPU 

Description: 

The proposed project will provide a first-year CS student with the opportunity for a friendly and approachable introduction to the world of high-performance computing (HPC). This research will introduce a student with no prior HPC experience to a higher level of programming and architectural understanding by studying one of the most widely used and computationally intensive mathematical operations applied in multiple scientific scenarios: matrix multiplication. Matrix multiplication is at the core of many routines in artificial intelligence, data science, and several other fields. 

The project aims to develop parallel thinking and a solid understanding of the two most dominant computational architectures: CPU and GPU. Additionally, the project will not only allow the student to acquire new marketable skills but also provide hands-on experience with state-of-the-art, multi-million-dollar hardware by granting access to the largest academic HPC cluster in the nation 

Mentor: Dr. Gupta 

Email: dittika.gupta@msutexas.edu

Department: Undergraduate Education 

Project Title: Analyzing Task through a Framework: Pathway for Learning 

Keywords: Task Analysis, Integration, Cultural Relevant Teaching  

Description: 

This research examines pre-service teachers' (PSTs) task design through the Cognitive Demand Cultural Connection (CDCC) framework, adapted from Smith and Stein (1998) and Matthew et al. (2022). With an increasing emphasis on culturally relevant teaching, it is essential for teacher education programs to prepare elementary PSTs to design mathematics tasks that integrate students’ cultural backgrounds, experiences, and interests Association of Mathematics Teacher Educators, 2017; Ladson-Billings, 1995; National Council of Teachers of Mathematics, 2020). The CDCC framework provides a structured approach to evaluating the integration of cognitive demand and cultural connections in mathematics tasks. The framework consists of four levels that assess how tasks engage students intellectually while also making relevant cultural connections through cognitive demand of the task and the cultural connections.  

This study seeks to uncover how well these tasks balance high cognitive demand and strong cultural connections. The intersection of these two components is particularly important, as it offers insights into how PSTs design tasks that are both intellectually rigorous and culturally meaningful. The findings can inform teacher education programs on how to better scaffold pre-service teachers (PSTs’) learning experiences, equipping them with the tools to create equitable and engaging mathematics instruction. The use of the CDCC framework allows for a structured evaluation of PSTs’ ability to integrate cognitive demand and cultural relevance, highlighting areas of strength and opportunities for growth. This would help educators to examine the task to enhance it for high cognitive demand and cultural connection. Findings from this study have the potential to inform curriculum development, instructional strategies, and professional development within teacher education programs. 

A key component of the culmination of this study would be the role of a student researcher, who will serve as an independent analyst coding the tasks based on the CDCC framework. The student researcher will practice research methodologies related to qualitative data analysis, including coding, organizing, and interpreting data. They will work closely with faculty to ensure methodological consistency and participate in intercoder agreement and member checking, strengthening the reliability and validity of the research. The student researcher’s findings will be compared and contrasted with the faculty mentor’s findings for validity and themes. Through this process, the student researcher will gain hands-on experience in analyzing results, refining their ability to assess and synthesize qualitative data. 

Ultimately, the study will result in a publication that presents the findings, with the student researcher playing an integral role in its development and serving as a co-author of the publication. By participating in the full research cycle—from data collection and analysis to dissemination of findings—the student researcher will gain a comprehensive understanding of educational research. This opportunity provides an invaluable foundation for future scholarly work, fostering analytical and critical thinking skills essential for engaging with research in education and beyond. 

Mentor: Dr. Elsharafi 

Email: mahmoud.elsharafi@msutexas.edu

Department: McCoy School of Engineering 

Project Title: Using Spearman's Rank Correlation method to determine the relationships between water quality parameters and their suitability for water membrane distillation and/or electricity generation. 

Keywords: Water Quality, Data Analysis, Water Membrane Distillation, Energy 

Description: 

The demand for energy consumption is increasing potentially. Oil and Gas is yet considered the main energy deriver source. In the process of oil/gas production, there are large amounts of water released into the environment.  It has become vital to utilize this water for the best interest of mankind. This study explores the feasibility of utilizing produced water from oil wells for water membrane distillation and/or electricity generation. Key water quality indicators such as pH, conductivity, free ammonia concentration, and total alkalinity are analyzed to determine their impact on the efficiency of membrane distillation and energy generation. The study shows how statistical analysis, even with a limited number of samples, can provide valuable insights into the best utilization of produced water. Given the limited availability of samples, Spearman's Rank Correlation method is applied using Microsoft Excel to identify key relationships between water quality parameters and their suitability for these applications. 

Mentors: Dr. Azzouz and Dr. Pokharel

Email: salim.azzouz@msutexas.edu | pranaya.pokharel@msutexas.edu

Department: McCoy School of Engineering 

Project Title: Data Collection from a Dual Venturi Wind Turbine 

Keywords: 1) Vertical wind turbines, 2) Dual Venturi airflow systems, 3) Wind tunnel testing 

Description: 

This research project is designed to test a Dual Venturi Vertical Axis Wind Turbine (DVVAWT) performances at different wind speeds. The wind turbine will be placed into the McCoy School of Engineering closed wind tunnel and tested at different wind speeds and different loading conditions.  The envisioned tests will tell whether or not the addition of a converging-diverging nozzles, i.e. Venturi systems, to the top and bottom of the DVVAWT will increase the operating efficiency of the device or not. Many studies have shown that in general Vertical Axis Wind Turbines  (VAWTs) are less efficient than Horizontal Axis Wind Turbines (HAWT). Thus, if this project is successful, and there is and effective increase in the DVVAWT efficiency, this research will demonstrate that DVVAWT technology could be a viable solution to the limited power generation capabilities of HAWTs. In fact this type of design will eliminate the generator power restriction associate with HAWT, and a high power generator can be placed on the ground for a DVVAWT. It is hoped to see that the low pressure zones created immediately above the turbines due to the top and bottom Venturi effect will cause an increase in wind speed and momentum transfer inside the turbine housing, thus enhancing its efficiency. This design uses vanes to direct the flow evenly across a mixed-flow turbine which is used to change the direction of the incoming flow from radial to axial. The model will be 3D-printed using the SLA technology. The technology is easy of use and yields a good surface finish. The surface finish is important because it minimizes the airflow friction against the internal walls of the wind turbine and consequently optimizes its efficiency. The design will also be highly modular so that different configurations of turbine rotors, blades, guide vanes, intake modules, and Venturi modules, can easily be swapped in and out. The shaft of the DVVAWT will be attached to a generator, the voltage, current, and power will be collected using a Raspberry Pi running a Python script. Using the collected data, a power-coefficient versus tip speed ratio graph will be plotted. The plot is a key metric used to determine the efficiency of the newly built DVVAWT. 

Mentor: Dr. Mahmud 

Email: kashif.mahmud@msutexas.edu

Department: Geosciences 

Project Title: Comparative Analysis of Greenhouse Gas Emissions from U.S. Transportation Sector 

Keywords: California, Climate change, Electric vehicle, Global warming, Texas 

Description: 

The transportation sector is one of the largest contributors to anthropogenic U.S. greenhouse gas (GHG) emissions. Transportation accounted for the largest portion (28%) of total U.S. GHG emissions in 2022. Cars, trucks, commercial aircraft, and railroads, among other sources, all contribute to transportation end-use sector emissions. Within the sector, light-duty vehicles (including passenger cars and light-duty trucks) were by far the largest category, with 57% of GHG emissions. Between 1990 and 2022, GHG emissions in the transportation sector increased more in absolute terms (18.4%) than any other sector (i.e., electricity generation, industry, agriculture, residential, commercial), due in large part to increased demand for travel. This fraction is the largest (54.6% increase) in Texas, whereas California had a reduction (4.9%) of transportation GHG emissions during this timeframe.  

Texas and California have consistently been the two biggest transportation-related carbon polluters in the U.S. In 2022, they emitted 219 and 183 million metric tons of carbon dioxide respectively. Combined, these two states – the most populous in the U.S. – account for almost a quarter of U.S. transportation CO₂ emissions. Transportation emissions make up one-fourth of Texas’ total GHG emissions and 80% of this emission comes from light-duty vehicles. Texas emits more GHGs than any other state, while California has made efforts to reduce its transportation emissions. California has reduced transportation GHG emissions through regulations, new technology, and policy changes, among other initiatives. Through the Alternative and Renewable Fuel and Vehicle Technology Program (ARFVTP), the Energy Commission provides about $100 million each year to develop and deploy low-carbon fuels, infrastructure for zero and near-zero emission vehicles, and advanced vehicle technologies. Therefore, our project will utilize various data products related to GHG emissions, transportation, population, economy, etc. to investigate the correlation between light-duty vehicle GHG emission and other demographic, economic, and policy-related factors.  

We aim to compare the light-duty vehicle GHG emissions of the two largest transportation-related carbon polluters (Texas and California) in the U.S. We hypothesize electric vehicles (EVs) have helped reduce GHG emissions in California by producing fewer tailpipe emissions than conventional vehicles. We expect population and economy have persisted similar growths for both these large states during the last three decades, however because of more effective policy decisions to significantly reduce transportation-related fossil fuel demand and emissions in California have maintained the state’s goals to achieve carbon neutrality by 2045 and avoid the worst impacts of climate change.  

The student will retrieve yearly transportation GHG emission data from 1990 to 2022 using the U.S. Environmental Protection Agency website. For the same time frame, the student will access demographic, economic, and population records from the U.S. Census Bureau data portal. Further transportation data will be downloaded and processed from both Texas and California’s Department of Transportation’s open data portals. We will then explore all these time series, using data from more than three decades to find the growth and patterns for all these variables. We will examine the transportation GHG emissions and the components of these emissions for the entire U.S., and the states of Texas and California. Moreover, we will investigate the correlation between light-duty vehicle GHG emissions in both these states with all possible growth factors (such as population, economic, transportation, light-duty vehicles, EVs, etc.). Finally, we will summarize the policies taken by both states to combat light-duty vehicle GHG emissions, hence global warming and climate change, and how these policies impacted the overall change of GHG emissions. 

Mentor: Dr. Rosscoe 

Email: steven.rosscoe@msutexas.edu

Department: Geosciences 

Project Title: Crinoid Conundrum - Describing and Evaluating Variation in Complex Crinoid Columnals 

Keywords: Invertebrate Paleontology, Fossils, Pennsylvanian 

Description: 

Ongoing research in Palo Pinto County, Texas, has revealed a wide variety of crinoid columnals (stem segments) that are poorly studied due to the current use of other parts of the crinoid animal in determining species. These columnals have been broken into two groups for study. Simple columnals are predominantly circular or pentagonal in shape (these columnals were evaluated in a Spring 2025 EURECA project). Complex crinoids have shapes that are not as simple to assess (ovate, creased, and deformed shapes). In order to further assess the diversity and potential interpretive value of these highly variable columnals, this project will evaluate a collection of complex crinoid columnals collected from the Mineral Wells Fossil Park. We will establish a standardized approach to specimen measurement and description that will enable proper analysis of these complex forms and comparison to the simple forms previously studied. Each specimen will be photographed using a digital microscope from multiple angles. Scaled images will be imported into ImageJ for precise measurement of the specimens. Specimens will be grouped into populations based on their morphological characteristics (ornamentation, gross shape, etc.) Populations will be compared graphically as a first approach at determining the potential relationships between the different columnal morphologies. We will be looking for populations that plot on their own distinctive growth curves, populations that plot along similar growth curves, and populations the exhibit no correlative pattern of growth. Populations that are distinct from others may have potential value in taxonomic assessment (identifying species or genera). Populations that plot along similar growth curves may indicate potential ontogenic (growth) or dimorphic (species with multiple forms) relationships. Populations that plot with no correlative trend will indicate a need for revision of methods or an incomplete/biased fossil record. If time permits comparative statistics may be used to further evaluate the relationships between populations.  

Name: Dr. Price 

Email: jonathan.price@msutexas.edu 

Department: Geosciences 

Project Title: X-ray visualizations of crystal lattices 

Keywords: X-ray diffraction, minerals, crystals 

Description: 

X-rays can be used to evaluate the structure of atomic arrangements (lattices) in crystals. X-ray powder diffraction (XRPD) is a technique that rotates a pulverized sample to an x-ray, such that an intense signal is received  when the angle relates to a spacing between atomic planes. The resulting “pattern” can be evaluated to determine the exact geometries of the lattice. These geometries impact macroscopic (hand-sized) properties, such as the exterior shape of the crystal. 

This UGROW project will examine well-formed crystal examples of two important minerals, zircon and wulfenite using XRPD. The study will use the diffractometer to characterize examples and determine key plane spacing, and then use processing software to visualize the structure. Work will occur in conjunction by a project under Dr. Cobb (Mathematics) to focus on attributes of the macroscopic geometries of these minerals. 

The student researcher will be fully involved in characterizing these materials, as well as optimizing the characterization process. The researcher will learn facets of x-ray instrument physics, analytical geochemistry, preparation of crystalline materials, contamination reduction techniques, fluorescence resolution, and mineral properties; no prior knowledge of these topics is required. Successful work will produce novel datasets and a handful of visualizations of the atomic structure of these minerals. 

Mentor: Dr. Harris 

Email: eboneigh.harris@msutexas.edu

Department: Institutional Effectiveness 

Project Title: Predictors of Student Success 

Keywords: multiple regression model, correlational analysis, data analysis, institutional research, student success, predictors, SPSS, Power BI 

Description: 

This purpose of this project is to create a regression model of student success predictors for MSU students. This project will involve interviewing campus stakeholders to define student success and identify possible correlates to student success, gathering and cleaning data, and running correlational analysis to identify the predictors that will be included within the regression model. In the four week time frame, the student researcher will likely have time to run correlational analysis on a few select variables they identify with support from the IE office. There may also be opportunities to use data visualization software to present and share findings. 

Mentor: Dr. Cobb 

Email: sarah.cobb@msutexas.edu

Department: Mathematics 

Project Title: Models and Visualization of Crystalline Structure in Minerals 

Keywords: geometry, modeling, crystallography, x-ray diffraction 

Description: 

The goal of this project is to develop novel digital and/or physical models to illuminate the molecular structure of crystalline materials, particularly minerals such as wulfenite and zircon. The project is closely related to Dr. Jonathan Price’s project characterizing these lattices using x-ray diffractometry. 

The defining characteristic of a crystalline material is atoms arranged in a three-dimensional repeating lattice. In addition to regular repetition, most lattice structures display reflection, rotation, or inversion symmetry. The lattice structure determines almost every property of a mineral, and understanding the lattice is crucial to understanding the material. Since direct observation of an atom-scale lattice is difficult, using measured properties to determine its structure is an essential scientific process. In particular, x-ray diffraction reveals data about spacing of key planes in the lattice, and careful analysis can determine the underlying structure. 

This project proposes to explore the connection between atom-scale lattice structure and more directly observable characteristics of a crystal. The student researcher will use both publicly available data and data from MSU’s x-ray lab to produce compelling digital models of crystals and their lattices. Once the digital model is produced, the focus will turn to representing important properties of the mineral that are directly reflected in the lattice. Ready availability of 3D-printed materials, precisely cut paper, and 3D digital renderings provide opportunities to develop novel visual and tactile materials useful for future students and scholars in mineralogy. 

Some familiarity with trigonometry will be needed to create the digital model; no other background is required, though knowledge of mineralogy, mathematics, or programming can certainly be applied to the project. 

Mentor: Dr. Carrier 

Email: zora.carrier@msutexas.edu 

Department: Wichita Falls Museum of Art at Midwestern State University 

Project Title: Digital Innovation in Museum Engagement

Keywords: museum and engagement, augmented reality (AR), virtual reality (VR) , interactive, displays, immersive and personalized experiences, mobile applications  

Description: 

Museums play a vital role in preserving and sharing history, culture, and knowledge. With the rise of digital innovation, technology has become an essential tool in shaping the way visitors engage with museum content. From augmented reality (AR) and virtual reality (VR) to mobile applications and interactive displays, these advancements provide immersive and personalized experiences that make learning more dynamic, accessible, and enjoyable. 

This research will provide valuable perspectives on how digital tools can create meaningful and inclusive museum experiences, ensuring that institutions continue to connect with diverse audiences in the digital age. 

This project will explore the role of technology in enhancing museum experiences, focusing on its impact on visitor engagement, accessibility, and education. Student researchers will investigate key questions such as: 

1. How is technology changing museum experiences? - Understanding the broader influence of digital tools on the museum field, including their role in making collections more interactive and accessible.
2. What is augmented reality in museums? - Exploring how AR overlays digital content onto real-world artifacts, allowing visitors to engage with exhibits in new and exciting ways.
3. How does virtual reality enhance visitor engagement? - Examining how VR creates immersive environments that transport visitors to different places and historical moments.
4. How do mobile apps benefit museum visitors? - Investigating the ways in which mobile applications enhance navigation, provide deeper content insights, and offer personalized recommendations.
5. What are the advantages of interactive displays in museums? - Analyzing the effectiveness of touchscreens, motion-activated exhibits, and AI-driven personalization in making museum visits more engaging.

Planned Activities 

Student researchers will engage in a variety of activities, including: 

• Literature Review: Reviewing case studies, articles, and reports on technology’s role in museums.
• Interviews & Surveys: Conducting discussions with museum professionals and collecting visitor feedback on technological experiences.
• Hands-on Technology Exploration: Testing existing AR/VR applications, mobile guides, and interactive displays to analyze their impact.
• Prototype Development (if applicable): Exploring simple AR or app-based solutions to enhance museum engagement.
• Presentation & Reporting: Summarizing findings in research papers, presentations, or digital reports.

Expected Outcomes 

By the end of the project, student researchers will: 

• Gain insights into the evolving role of technology in museums.
• Develop critical analysis and research skills.
• Contribute to discussions on best practices for museum engagement.
• Potentially create recommendations or prototype ideas for technological improvements in museums.

Mentor: Dr. Lee 

Email: juheon.lee@msutexas.edu 

Department: Political Science 

Project Title: Environmental Performance and Varieties of Democracy: An Exploratory Cross-National Study 

Keywords: Environmental Performance; Varieties of Democracy; Climate Change; Environmental Health 

Description: 

Many previous studies have investigated the relationship between national-level environmental performance and the quality of democracy. Contrary to their expectations, however, findings have not been consistent. While some studies find that democracy increases the likelihood of achieving sustainable development, others argue that democracy actually has negative effects on environmental performance. The so-called “authoritarian environmentalism” thesis suggests that authoritarian governments are better at protecting the environment than democratic governments that are under constant pressure from various economic interests.   

Build upon the previous studies, this study assumes that not every aspect of democracy is conducive to promote sustainable development; therefore, we need to go deeper and compare how different governmental contexts have different impacts on the countries’ environmental performance. In support of our assumption, a growing number of scholars emphasize the importance of governmental contexts—such as political stability, party structure, or regulatory quality—that influence governmental performance at the national level. Therefore, we will investigate how such contexts are associated with environmental performance through a cross-national study. 

In order to conduct our study, our team will rely on two large-sized public datasets. The first dataset is Environmental Performance Index (EPI), managed by Yale Center for Environmental Law & Policy, which provides 58 environmental performance indicators of 180 countries, including environmental health, climate change, and ecological system. The second dataset is Varieties of Democracy (V-Dem), managed by the University of Gothenburg, which provides extensive data on governmental structure, organizational capacity of political parties, election systems, etc. Throughout the UGROW program (if accepted), the mentor and mentees will work on combining the two big datasets and explore how different governmental structure and party and election systems are associated with their national environmental performance indicators. Our original findings will be visualized to be presentable at academic conferences. Our final goal is to contribute to the literature in environmental politics & policy-making.