Trap-Neuter-Release (TNR) is one of the most widely accepted forms of reducing stray feline populations. However, it still faces a lot of controversies as many different groups argue for its effectiveness. Some argue for Trap-Neuter-Euthanize (TNE), while others argue that Trap-Neuter-Vaccinate-Adopt (TNVA) is the most humane way to deal with the rapid influx of feline stray animal overpopulation in the United States. This essay aims to investigate the three most common ways to handle animal overpopulation and hopes to shed light on the costs and benefits of these programs. The reason why stray animal overpopulation is a very important issue is because felines pose a health risk to not only themselves, the environment, but also to humans. Feline overpopulation has been steadily increasing and a solid solution needs to finally be proposed and implemented. 

The goal of my research is to investigate and engineer co-crystals from derivatives of N-2,1,3-benzochalcogenadiazole-4-ylbenzamide (BCD Benzamide). By examining a variety of crystal structures from the same family, we can predict and design new systems with tailored molecular interactions, including those for co-crystal formation. This method allows us to design desired crystals with enhanced properties, while negating the need for complex covalent synthesis. Once enough derivatives have been analyzed, we will be able to draw conclusions about how molecular structure affects supramolecular structure, and hopefully predict the crystal structure of other derivatives as well as more easily create co-crystals. This project has potential applications in materials science (specifically optoelectronics) as well as pharmaceuticals. 

In this project, we present a strain-amplifying mechano-luminescent mechanical metamaterial (SAM3) to broaden application of mechano-luminescent (ML) composites as a strain sensing composite by overcoming its intrinsic limitations due to high threshold strain and strain rate for ML light emission. Through finite element analysis, candidate SAM3 designs are suggested after evaluating strain-amplifying capabilities of SAM3s using strain-amplifying ratios and strain-amplifying limits. The SAM3 designs are manufactured by printing customized ML resin using a liquid crystal display-based stereolithography 3D printer. The prototypes are then subjected to uniaxial tension tests while recording the ML light emission from the SAM3 prototypes using a camera. In addition, the mechanical properties of the printed ML composites are characterized. 

The lightning return stroke happens when lightning channels attach to a ground structure. A powerful surge of current travels from the ground towards the cloud using the precursor channels as a waveguide. This powerful surge transforms the waveguide by heating of the atmospheric air and the resulting expansion that results in thunder. The return stroke is the most destructive element of a lightning flash: capable of igniting fires and grounding airplanes. In this work, we model the lightning return stroke using the Telegrapher’s Equations, which describe distributed RLC circuits. The partial differential equations are solved analytically with a combination of d’Alembert and Riemann methods. However, this is only practical for constant coefficients. A finite-difference, numerical solution is devised to solve the problem in the general case. The model shows how resistance plays a key role in the return stroke current wave dynamics. The model also explains the origin of the well-known channel base current waveform: the current rise time is associated with the attachment process, while the fall time is due to current decay in the resistive channel. 

The EZIE-Mag citizen science program provides participants with a magnetometer kit for studying variations in Earth’s magnetic field. Upon assembling and insulating the device, it was deployed for continuous data collection beginning in early December 2024. To analyze the collected data, we developed a Python program to parse the EZIE-Mag’s data files and generate plots of magnetic field along each axis, as well as temperature, against time. The data clearly shows the daily fluctuations in the geomagnetic field caused by ionospheric currents driven by solar heating known as the diurnal ionospheric Solar-Quiet (Sq) variation. The magnetometer should also be able to detect variability in the geomagnetic field derived from geomagnetic disturbances like geomagnetic storms caused by different space weather events like coronal mass ejections (CMEs), solar wind variations and magnetospheric currents. We used comparisons with nearby geomagnetic stations and data modeling, to find signatures of these events in our data. Our findings highlight both the potential and constraints of using low-cost, citizen-science magnetometers for broadening our understanding of ionospheric dynamics and space-weather, underscoring the importance of refining calibration techniques and long-term data collection. 

Commercial space flights, reusable vehicles, and booster rockets are becoming increasingly common as the National Aeronautics and Space Administration (NASA) collaborates with commercial space companies to resupply the International Space Station (ISS) and provide civilians with spaceflight experiences. Given the extreme space environment, characterized by extreme temperatures, vacuum, and radiation, it is crucial to monitor the health of commercial space vehicles and their reusable components. Structural Health Monitoring (SHM) can be employed to assess the readiness of these vehicles and their parts for return to flight. Circular piezoelectric sensors (PZTs) are commonly used in SHM, and their characteristics must be accurately determined. However, manufacturers typically specify material properties that can vary ±20%, which is too broad for assessing the effects of the space environment on these sensors. This study utilizes dynamic measurements of PZT sensors to enhance accuracy. Impedance spectra of PZTs are obtained, and key parameters, including sound speed, piezoelectric coefficient, electric permittivity, elastic compliance, and planar coupling, are calculated. Average values of these constants, along with their statistical deviations, are determined and compared to available data. 

New Mexico Tech’s Earth and Environmental science Department’s fossil collection was first established by Dr. Christina Lochman-Balk in the 1960s and 70s, and has been used in many paleontology and Earth history classes over the years. Since 2019, we have been working to catalog and revitalize the collection and make it a sustainable resource for students of New Mexico Tech. We reorganized the collection by period, and then by phylum within each period. We have been cataloging the fossils in the collection into a large spreadsheet database, including creating internal ID numbers for each specimen, and then comparing against earlier records to identify missing specimens and determine if there are important gaps that we need to fill by acquiring new fossils. The collection has unique features that make it a useful resource for geology and biology courses, including representative fossils from across New Mexico that allow students to explore the state’s biota across the Phanerozoic, especially marine deposits from the Carboniferous and the Permian when New Mexico was underneath an inland sea. Furthermore, now that the collection has been reorganized, we have identified several potential uses for education and outreach beyond NMT classes. These include in-person outreach activities and lesson plans for K12 students, social media and website resources on sedimentary rocks and fossil reefs that we plan to develop with the National Cave and Karst Research Institute (NCKRI), and other public education activities about fossils and past environments in New Mexico. 

Symbiotic stars are a type of binary star system in which a smaller and denser star, usually a white dwarf, orbits and accretes mass from a larger red giant companion. There are two main methods of mass transfer between stars in a symbiotic binary: Roche-lobe Overflow (RLOF) and Wind Roche-lobe Overflow (WRLOF). A Roche-lobe is the region surrounding a star in which matter is gravitationally bound to its core. In the case of RLOF, the red giant’s Roche-lobe is filled, usually through the process of stellar evolution, causing any extra matter that extends past the region of the Roche-lobe to flow to the white dwarf companion through the first Lagrangian point. For WRLOF, the matter from the red giant extends past the region of the Roche-lobe due to stellar winds pushing the stellar material, causing it to then be accreted by the white dwarf. We aim to analyze the star HD 352, which has previously been shown to be undergoing Roche-lobe Overflow, and apply new tools to confirm if RLOF has occurred. 

Stratospheric airships face significant challenges in trajectory control due to their underactuated, coupled dynamics, large inertia, and long response delays. These issues are exacerbated under windy conditions where system dynamics become nonlinear and uncertain, reducing the effectiveness of conventional control methods. Traditional linear control strategies, such as PID controllers, lack adaptability and robustness to environmental changes, while nonlinear model-based controllers require accurate system modeling, which is often impractical for complex, high-dimensional systems. To address these limitations, we propose a hybrid control framework that integrates reinforcement learning (RL) with model-based Model Predictive Control (MPC). This approach leverages RL to compensate for model inaccuracies, improve adaptability to dynamic disturbances, and correct MPC strategies using data-driven insights. Furthermore, RL is used to accelerate value function computation and enhance system safety by accounting for actuator faults, input constraints, and external disturbances. This hybrid method aims to provide a robust, adaptive, and fault-tolerant control solution for real-time trajectory tracking in challenging environments. 

This study aims to present a physical digital twin (PDT) built using in-plane strains measured on a human body with a highly flexible mechano-luminescence-optoelectronic (MLO) sensor. The developed PDT is envisioned to provide accurate virtual representations of actual physical movements in biological structural systems that automatically update based on data from the MLO sensor. By integrating an in-contact MLO strain sensor, this PDT can advance knowledge about the physical movements of highly dynamic and individualized biological structural systems without relying on expensive and bulky non-contact body monitoring equipment. For validation of the PDT, a human subject performed a controlled exercise while an MLO sensor placed on the knee joint recorded direct current (DC) output. A high-speed camera was used to capture knee motion, providing reference data for strain approximation based on changes in knee angle. The collected data underwent pre-processing and synchronization and was subsequently used to train a neural network, with DC strain sensor readings serving as input features and calculated strain values as targets. The neural network model demonstrated promising accuracy, achieving a mean absolute error of less than 5% across all tested conditions. The study further explored the impacts of sensor placement, reading noise, and synchronization delays, identifying potential mitigation strategies such as enhanced signal processing and multi-sensor fusion techniques. The findings highlight the feasibility of using MLO-based sensors for real-time strain estimation and lay the groundwork for future advancements in biomechanical monitoring, personalized rehabilitation, and health assessment through PDT systems. 

Hydrogels are a network of hydrophilic polymers that are able to retain large amounts of water thus making it an ideal material for various biomedical applications, specifically wound dressing. Applying hydrogels as wound dressing offer many advantages like increasing moisture retention which promotes an environment for healing. This study investigates a hydrogel composed of a blend of natural and synthetic polymers, specifically chitosan, alginate, and poly(methacrylic acid). With this combination we can aim to leverage the unique benefits of each polymer including biocompatibility, moisture retention, and mechanical strength. To determine the characteristics of the hydrogel we are evaluating through a series of different characterization techniques. Swelling and degradation tests to assess the moisture retention and possible biodegradability, stress and strain analysis to evaluate the mechanical properties, solid state NMR for insights into the structure. Along with using Skin Scratch Assay to understand cell viability and migration. These will provide a detailed understanding of these hydrogels and the possible applications to wound dressing 

In this investigation, detonation characteristics of C4 charges were modeled using hydrocode modeling to create repeatable simulations that accurately reflect and minimize discrepancies between the simulation and experimental data. Of specific interest here is the interaction between the fireball expansion and the ground. Experiments using piezoelectric pressure transducers and high-speed imaging utilizing a Background Oriented Schlieren (BOS) were used to gather data on ground burst explosions ranging from one meter to two meters. Image processing techniques were developed to quantify fireball dimensions.

The pressure results gathered from simulation and experimental show similar trends and behavior with some discrepancies. This leads to reconsidering equation of state values to be used to accurately recreate the experiments with the simulation.

With the expanding usage of AI, a branch of artificial intelligence, called computer vision, can be utilized by other fields. The main use of artificial intelligence in Civil Engineering ranges from damage detection to condition assessment of civil structures. NMT has partnered with the NMDOT with its CAMP (Culvert Asset Management Plan) initiative in order to help assess culverts across New Mexico. The current method used to determine the geolocation of culverts is to send out teams who visually identify culverts on-site and collect GPS coordinates on devices, but human error leads to poor quality data. The main concern, in this project, is locating culverts, as many culvert locations are inaccurate, missing, or not accounted for in the first round of culvert inspections. In an attempt to capture the missing cohort of culverts, an AI model can cross reference collected geolocations with a street view screenshot. The process to identify culverts involves a Python script using Google Maps annotated using label imaging software (LabelImg) for locations. This creates the data for where culverts should be. The AI model compares if a culvert should be located within proximity or if one is missing and flags the location for review. When this model is created, it can help improve data quality in NMDOT’s culvert database and worker efficiency. 

Schlieren imaging is a widely used optical technique for characterizing density gradients in fluid materials, particularly gases. It operates on the principle that light bends when encountering a medium of different density, refracting toward the denser material. Traditional schlieren systems assume a uniform testing medium, but when multiple mediums are present, their impact on results must be characterized. spectroscopy, which analyzes light absorption at specific wavelengths unique to a material or reaction, is commonly used in analytical chemistry and astronomy. By integrating these techniques into a single process, it becomes possible to determine both the material composition and density field of a schlieren system using the gaseous component of a spectroscopy setup. In this study, a calibrated schlieren-spectroscopy system was used to analyze light wavelength bands, characterizing Helium and Helium-Iodine varied-density plumes. These flow fields were then used to replace the spectrometer with color filters, creating a purely schlieren-based system capable of correlating color-filtered light to medium concentration and material absorbance—effectively performing spectroscopy without a spectrometer. 

With the advent of large scale survey telescopes, a cost effective followup system is apparent. Solely with the advent of the Vera C. Ruben observatory, the number of transients expected per night are on the order of 10^7. On any given night there may be as many as 10,000 every two minutes. Having a system that specializes in fast response followup observations is crucial. The Wide field infrared Kuiper belt and Exoplanet explorer telescope is being designed to fulfill the role of remote, low response time followup observation. So far the work has produced an 80mm prototype and advancements such as the development of a direct axial flux high resolution motor (DAFR) and, potential 3-D printable mounts. This 80mm testbed will be able to provide critical information about the DAFR system more specifically how it will perform under a wide variety of conditions, pointing errors, mechanical failure points, and internally generated vibrations. When the prototype is complete it will provide a background for the sensitivity of near infrared imaging in the 0.7-2.1 micron band. Work on the final 0.7 meter optical system design has shown that to satisfy the requirements of both being high resolution and wide field by using a thin removable meniscus corrector lens. When the system is being run under the subsecond wide field configuration, the thin lens would change the available flat area from 40 square arcminutes to 4 square degrees. 

This semester marks the beginning of New Mexico Tech’s four-year development of a CubeSat with the project's science goal of studying earth's ionosphere and plasma-sphere. In this early stage, the teams’ efforts are focused on understanding the science goals and mission performance requirements. From those we are identifying subsystems and developing subsystem requirements needed to create a successful CubeSat mission . Core teams currently are Communication, Power Systems, Computer Science, Mechanical, Navigation , HF beacon and VLF Receiver. By the end of this semester our team will have the foundational understanding of the project and subsystem requirements along with identifying project constraints to create an effective CubeSat. 

This study evaluates the CO₂ storage potential of the SJD fractured granite reservoir through advanced reactive transport modeling, focusing on the impacts of fracture-matrix heterogeneity and pre-existing fluids. The shallow Precambrian granite system presents unique challenges for carbon sequestration, including fracture-dominated flow pathways, weathered matrix, and native CO₂ accumulation. The study hypothesizes that while fracture networks control initial plume migration, the weathered matrix dominates long-term trapping, and that pre-existing CO₂ reduces mineral trapping while enhancing solubility trapping through brine acidification. The study employs CMG-GEM to model multi-phase flow and geochemical reactions between injected CO₂, formation fluids, and reservoir minerals. The model incorporates fracture-matrix mass transfer, key mineral reactions such as feldspar dissolution and carbonate precipitation, and caprock stability assessment under CO₂ exposure. Results demonstrate that fractures account for most early-stage CO₂ migration, while capillary trapping in the matrix contributes to long-term storage capacity. Pre-existing CO₂ reduces available mineral trapping surfaces but enhances solubility trapping through brine acidification. The clay-rich caprock shows localized alteration at high-flux zones while maintaining overall integrity. These findings confirm that fractured granite reservoirs can serve as effective CO₂ storage sites when injection strategies are optimized for their dual-porosity characteristics and in-situ fluid chemistry. The study provides critical insights for operational deployment in basement reservoirs, highlighting the need to balance fracture-dominated injectivity with matrix-dependent trapping while monitoring long-term caprock stability. This work establishes a framework for assessing CO₂ storage potential in complex fractured systems with pre-existing fluids. 

Uranium (U) contamination commonly results from munitions testing, historical conflicts, industrial processes, and mining. Depleted Uranium (DU), a form of U, is less radioactively harmful than natural U but still has mutagenic and toxic properties. While human health concerns related to DU toxicity are well established, effects of DU contamination on microbial communities essential for nutrient cycling are not, thus hindering potential remediation strategies for affected environments. Soil samples were obtained from the EMRTC legacy munitions testing site to determine the impact of DU on arid ecosystems. Five sample sites, including a control, were designated based on predicted DU concentrations and further measured with ICP-MS. The pH and electrical conductivity of the samples were measured while gravimetric analysis was conducted to determine the loss on ignition of organic carbon. Decomposition rates were observed with litter bag incubations, soil water retention was calculated, gas flux measurements for CO2, CH4, N2O were recorded, and soil NO3-/NH4+/PO43- were quantified using spectrophotometric analysis. These parameters give an overall glimpse into the microbial metabolic activity in DU landscapes. Preliminary results indicate a negative correlation between DU concentrations and levels of general soil nitrate, water, and organic carbon. Our poster will report gas flux data from the duration of the lab experiment and a complete statistical analysis of all data collected. Our conclusions provide valuable insights for plant-microbial-based DU remediation strategies with applications for uranium-contaminated areas in New Mexico. 

Storing carbon dioxide (CO₂) underground has become an increasingly important strategy in the effort to reduce greenhouse gas emissions. Around the world and across the United States in particular geological sequestration projects are helping to safely store CO₂ deep beneath the earth surface. Among the most promising storage sites are depleted gas reservoirs, which already have proven containment and well-documented geologic histories, making them strong candidates for long-term CO₂ injection.

This study examines a natural gas reservoir in the Permian Basin, now under consideration for geological CO₂ storage. A detailed geologic model was created in CMG software using well log data, production history, and pressure records. History-matched gas rates served as the foundation for forward simulations, which tested how the reservoir responds to long-term CO₂ injection. The assessment incorporated key reservoir properties such as porosity, permeability and pressure conditions to evaluate both injectivity and containment behavior.

Findings suggest that deep, structurally confined reservoirs of this kind are capable of safely storing substantial volumes of CO₂ over multi-decade timeframes. Simulation results indicate stable plume migration and manageable pressure buildup, with minimal risk to overlying formations. These insights reinforce the viability of repurposing depleted hydrocarbon fields for carbon storage and contribute to broader efforts aimed at building reliable, scalable CO₂ sequestration infrastructure. 

The St. John's Dome basement, composed of granite, presents a complex geological system where stress evolution, fracture dynamics, and long-term subsurface stability must be evaluated, particularly under fluid injection. Geomechanical modeling is essential for assessing subsurface stress changes, fault reactivation, and permeability evolution. Due to granite’s low porosity, high strength, and brittle deformation, a coupled thermal-hydro-mechanical-chemical (THMC) approach is required to capture stress-strain relationships and fluid-rock interactions. Granite's response to injection is influenced by in-situ stress and pre-existing fractures. Stress-induced microfracturing may enhance permeability while also concentrating stress locally. Prior studies show that permeability and fracture behavior in granite depend on injection pressure, temperature, and fluid composition. High-pressure CO₂ injection may induce shear failure along existing fractures, forming new flow pathways. Therefore, geomechanical models must incorporate multiscale stress analysis and long-term deformation behavior to accurately predict reservoir evolution. This study applies a reservoir computer model to simulate the stress-strain response of granite during extended fluid injection. The model integrates calibrated geomechanical properties such as Young’s modulus and Poisson’s ratio, using literature-based experimental data. Simulations are conducted for both dry and saturated conditions. Results reveal that fluid injection causes localized pressure variations, subsidence, and volumetric strain. These findings underscore the need for integrated modeling approaches to evaluate the long-term mechanical integrity and flow behavior in granitic formations under injection scenarios.