Presentations given by students that submitted to the research journal
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A Convolutional Neural Network for Atmospheric thermal detection in Unmanned Aerial Vehicles
Joshua Riojas
Birds utilize atmospheric thermals, which are vertical columns of warm air generated by the uneven heating of the Earth's surface, to conserve significant energy during flight, particularly over long migratory routes. This natural energy-saving strategy presents a compelling model for enhancing the endurance of Unmanned Aerial Vehicles (UAVs). This study aims to apply this biomimetic concept to UAVs to conserve onboard energy and thereby extend the drone's operational flight time. The primary objective of this research is to develop and validate a method for a UAV to autonomously detect and utilize atmospheric thermals using a machine learning model to predict the presence of these updrafts in real-time. A Convolutional Neural Network (CNN) model was developed to predict whether the UAV is in an atmospheric thermal based on vertical error derived from its XYZ coordinates. Furthermore, the model incorporates gyroscope data (roll, pitch, and yaw) to determine the UAV's position and orientation within the thermal. The quantitative analysis revealed that the CNN model accurately predicted the presence of atmospheric thermals by correlating vertical error with the UAV's spatial coordinates and gyroscope data. The successful implementation of the CNN demonstrates that it is feasible for a UAV to autonomously identify and orient itself within atmospheric thermals using standard onboard sensors. This step advances energy-efficient UAVs mimicking bird soaring, using natural updrafts to extend flight time, reducing battery dependence, and enabling long missions like monitoring, surveillance, and sensing.
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AI-Powered Content Strategy Implementation: Evaluating Effectiveness for Startups in Four Industries
Lilian Hernandez Peregrino
Startups face significant challenges in today’s rapidly evolving digital marketplace, particularly when competing with larger firms that can more readily absorb risk and adapt to technological change. Although established frameworks such as the Business Model Canvas (BMC) and Joe Pulizzi’s Content Inc. model provide valuable guidance for early-stage ventures, they do not fully address the demands of modern marketing in an era increasingly defined by artificial intelligence (AI). This qualitative multiple-case study examines how an AI-enhanced Content Inc. model can support more effective content marketing strategies for startups operating in four distinct industries: food, custom software development, outdoor consumer products, and grant management software. Using the BMC as a contextual framework, this research focuses on founders’ firsthand experiences and feedback to understand how AI tools influence the development and implementation of content strategies. Semi-structured interviews were conducted with the founders of each participating startup to capture insights on current practices, challenges, and the practical application of AI within a content-driven approach. Cross-case analysis was then used to compare how each industry adapted to the AI-enhanced model, highlighting which sectors may be best positioned for successful implementation. While the study’s findings are limited by its small sample size and an eight-week research period, the results aim to offer actionable insights for startups seeking to build sustainable growth through more efficient, technology-supported marketing strategies.
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AI vs. Human Imagery in Advertising: Do Consumers Care?
Belyn Thompson
Digital marketing has been evolving at rapid speed with Artificial Intelligence (AI) emerging through generative AI creating visuals for advertisement. Yet little is still known about how consumers truly feel about this change. This study explores whether AI-generated images truly differ from human-created photography in the eyes of everyday buyers, focusing on three key metrics: purchase intent, aesthetic appeal, and perceived realism. Over a roughly two-week window in June 2025, 199 U.S. adults on Amazon Mechanical Turk (MTurk) evaluated 24 product images: technology, food, apparel, and cosmetics. Each is presented in both AI “sibling” and original photographer versions under varying disclosure labels. Participants also completed preliminary questions about AI familiarity and brand trust. Results showed that, in most categories, consumers did not penalize AI visuals; only in apparel did the correct image disclosure trigger a marginally significant 0.499-point drop in purchase intent (p = .015). These findings suggest that, beyond novelty, high-quality AI imagery may be able to compare against human-created photos, except in contexts where authenticity and craftsmanship are especially prized. Marketers can therefore leverage AI as a creative collaborator and tool to help aid company success if used intentionally. Future work may investigate long-term AI exposure effects, delve into cross-cultural differences, and explore disclosure and other factors in relation to consumers’ perception and overall attitude.
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Detection of Catalytic Intermediates in the RuBisCO Mechanism using 13C and 31P NMR
Julia Martinez
Ribulose 1,5-bisphosphate carboxylase/oxygenase, or RuBisCO, is an enzyme which plays an essential role in carbon fixation and plant metabolism. While RuBisCO is considered the most abundant protein on the planet, its catalytic mechanism is still not completely understood. RuBisCO is notoriously slow and exhibits low specificity for CO2 over O2, leading to energetically wasteful photorespiration. Efforts to improve RuBisCO’s catalytic efficiency have been hindered due to an incomplete picture of key transient intermediates involved in the reaction. Acid quenched single turnover reaction experiments in combination with 13C and 31P Nuclear Magnetic Spectroscopy (NMR) offers a promising approach for the characterization of RuBisCO’s reaction pathway at a molecular level, including the detection of short-lived intermediates. In this study, we present an initial framework for applying NMR techniques to probe the RuBisCO catalytic pathway. We assess the feasability of detecting catalytic intermediates under varying experimental conditions, including cofactor availability and reaction time. Our preliminary data reveal both the promise and the technical challenges of this approach, including issues related to sensitivity, intermediate lifetimes, and sample conditions. While the direct observation of individual intermediates remains a target for future work, these findings demonstrate the potential of NMR as a tool for investigating RuBisCO’s catalytic mechanism and provide a technical foundation for future research aimed at the characterization of reaction intermediates.
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Do Female Belugas Aid in Sexual Development of Juvenile Males?
Alondra-Sophia Martinez
Reproduction is essential for every species to maintain their population. Observations of beluga (Delphinapterus leucas) reproduction in the wild and managed care have been limited, with few recordings of sexual behavior. Most data of sexual/sociosexual behavior have been of male-onmale interactions. This leaves a substantial gap in male and female interactions, specifically between adult females and juvenile/immature males. Footage collected between 2013 and 2025 were collected from SeaWorld, San Antonio, housing 17 beluga whales throughout these years, eight females (seven adults, one juvenile female), and nine males (two adults, seven juveniles). From about 21 hours of data, this study showed that adult females did initiate in sociosexual and sexual interactions with juvenile males during the breeding season. Adult females initiated more sociosexual interactions when there were fewer males in a social group but appeared to initiate more sexual interactions when there were 3-4 juvenile males. These social interactions suggest that adult females may potentially play a critical role in the sociosexual/sexual development of juvenile males.
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Do Young Beluga’s Play More or Less Than Adult Beluga’s?
Winter Saldaña
The presence of play is used as an indicator of well-being in many species including belugas. The purpose of the current study was to explore if object play exhibited by belugas varied between young and adult animals. Within the year 2023, 79 archived videos of the belugas at SeaWorld Texas were selected and coded for object play, which included water, enrichment devices, and organic material. Adult and young belugas engaged in a similar amount of play bouts and actions during object play. These findings suggest that belugas in managed care experience positive well-being as indicated by the presence of object play but do not address how object play emerges over development. Future research should explore the emergence of play to examine how development and experience influence actions with objects.
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Evaluation of Prosthetic and Orthotic Technologies using AI Technology
Iris Reyna
Background: Artificial intelligence (AI) is being used at all levels of education and career paths. In the healthcare field, biomechanical analysis is used to study human movement and evaluate the mobility and quality of life of individuals with certain interventions. An artificial intelligence system used to evaluate the health and mobility of individuals with a smartphone video is sit2stand.ai. This AI application provides sensor technologies at an inexpensive cost. Providing flexion time and trunk acceleration that is used in the modifications of prosthetics and orthotics to provided stability, ground reaction force, and injury risk to the user.
Methods: 10 patients were asked to participate in a research study of the evaluation of prosthetics and Orthotics. 5 individuals were lower limb amputees, and 5 individuals were ablebodied to evaluate the effects of the interventions on the individuals' mobility and quality of life.
The patients were asked to sit in a stable chair and to cross their hands over their chest and sit -and-stand 10 times while being recorded. The videos were then uploaded to the AI application sit2stand.ai, and data was collected for graphical analysis.
Results: The results show flexion time and trunk acceleration after collected from the AI application sit2stand.ai. The graphical analysis showed the overall comparison of each individual that participated in the research and the usability of sit2stand.ai.
Conclusion: Results suggest that the AI application sit2stand.ai is an effective, accessible, user and clinically friendly at evaluating lower limb prosthesis.
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Leveraging MATLAB for Genomic Analysis of Alzheimer’s Disease Risk Genes in GWAS Datasets
Rolando Ramos
Alzheimer’s disease (AD) is a complex neurodegenerative disorder influenced by both environmental and genetic factors. My research emphasizes a bioinformatics pipeline using MATLAB, a data analysis tool, to analyze genome-wide association study (GWAS) data intended to pinpoint key genetic variants involved in AD. This method allows for large-scale genomic interrogation while minimizing manual processing errors. We are particularly interested in loci linked to well-established risk genes such as APOE, BIN1, and TREM2. Publicly available datasets, including the Gene Expression Omnibus (GEO) and Alzheimer’s Disease Neuroimaging Initiative (ADNI), are being processed to extract and purify relevant genotypic and expression data. Mat lab serves as a versatile engine to organize, clean, and statistically evaluate the data, integrating functions from the Bioinformatics Toolbox to conduct SNP filtering, allele frequency analysis, and genomic variant visualization. Efficient comparison between control and AD samples are displayed through effective methodology. Differential expression and variant patterns support findings reported in current literature. Insights from this analysis may reveal patterns that inform both diagnostic strategies and therapeutic research. Early identification of high-risk alleles allow for more personalized and proactive clinical interventions. This approach not only streamlines data analysis but also provides a standardized and scalable framework for future investigations. We aim to capture a deeper understanding of the molecular basis of AD and support efforts in preventive health by identifying early genetic risk factors. This research underscores the potential of computational biology in advancing precision medicine and demonstrates how technical tools like MATLAB can be applied in genomic medicine.
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Mica Productions: The Sad Truth Behind Cosmetic Corporations’ Impact on Children’s Health in India
Nyleen Benavides
Mica is a shiny silicate mineral found in granite, crystals, or other rocks. Its shiny and reflective property has attracted the eyes of many cosmetic corporations. Due to high rates of unemployment and poverty in Asian countries such as India and its Jharkhand region, corporations have taken over and manipulated the exportations of mica. These regions carry out illegal child labor in mica mining sites that are not only hazardous to children, but the economy and ecosystem around them. Previous research has shown how cosmetic corporations use mica as one of their main ingredients for shiny pigmentations within their products, however, there is limited research that has indicated the links between child labor and the cosmetic industry. This research examines the effects that cosmetic corporations have on children in India. Specifically, it seeks to discuss why the high demand of mica increases child labor and negatively impacts the health, economic, and social wellbeing of children. By analyzing secondary sources such as peer reviewed articles, reports, and policies in India, this study will contribute to a better understanding of exploitative child labor and provide alternative ways corporations can mine mica that allows a more effective quality of life for children and their families.
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Modeling the Effects of Plasma Etching Parameters using MATLAB
Joshua Mendez
This paper presents a study of plasma etching in tungsten diselenide (WSe2), a transition metal dichalcogenide with promising applications in nanofabrication. We investigate the effects of ion flux, incidence angle, and energy distribution on etch profiles by developing a custom MATLAB simulation framework informed by literature-derived experimental data. The simulation models trench geometry evolution over time and includes visualization of etch depth in 1D, 2D, and 3D. Preliminary results qualitatively reproduce anisotropic etching trends reported in the literature. This work aims to provide a scalable computational tool to aid understanding and optimization of plasma etching parameters for emerging nanomaterials.
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Resisting “Chicana and Latina Syndrome”: Preserving Difference across Feminist Accounts of the Self
Amely Logan
This paper considers feminist Chicanx and Latinx philosophers’ theories of the self and takes up the call that we must resist the tendency to clump Chicanx and Latinx identity together. In this paper, I respond to the work of Latina feminist theorist, Carmen Lugo-Lugo, in which she addresses the dangers of homogenizing Chicanx and Latinx identity and insists that we must take seriously the differences between the two identities. While more attention has been given to Chicanx and Latinx work in recent philosophical literature, little has been done to track distinctions between Chicanx and Latinx theories of the self. I consult the works of Latinx and Chicanx feminist philosophers, including Mariana Ortega, María Lugones, Gloria Anzaldúa, Jacqueline Martinez, Norma Alarcón, and Linda M. Alcoff, in order to demonstrate the differences between their descriptions of lived experience and theories of the self. My account reveals that Chicanx and Latinx theories of the self-differ in three major ways: (1) in the history that gave rise to the term Chicanx, (2) in the differences between Latinx and Chicanx lived experience, and (3) in the worlds that Chicanxs and Latinxs belong to. This paper thus illustrates how, by failing to take seriously these differences, we risk further homogenization and/or erasure of multiply-marginalized selves.
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Sacred Silence: Religion and Intimate Partner Violence
Miranda Ibarra
This study investigates the Intimate Partner Violence (IPV) within the St. Mary’s University community. A survey distributed via institutional email collected responses addressing both direct and indirect experiences related to IPV. The analysis explores associations between religiosity, permissive IPV attitudes, and belief in divine control; results indicate no statistically significant relationship. Additionally, the study examined the connections between religiosity, IPV victimization, and frequency of perpetrated abuse, finding no significant associations with religiosity but a significant association between victimization and perpetration frequency. Limitations include the use of a convenience sample, restricted generalizability, and potential self-report bias, which may affect the reliability of the data.
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Separate But Found: An Anthropological Analysis of Toni Morrison’s Beloved
B. Haynes
Separation is the ideal solution for African American women to recover their humanity and build a future in America, according to Toni Morrison's Beloved. African American women assimilating with their White oppressors is not advantageous to their survival, and this is revealed through the extremely different experiences that Baby Suggs, Sethe, Beloved, and Denver have had with Whiteness, experiences that have irrevocably altered their lives and viewpoints. These women understandably harbor a strong hatred for their oppressor and would be unable to foster a coexistence with them, given their suffering after being the victims of sexual assault, lynchings, hard labor, substandard living circumstances, and death regularly. To demonstrate the methodical methods in which women are dehumanized under Whiteness and the reasons for the separation, this essay examines each lady, their most pivotal moment, and their experiences within the book through Agamben's philosophy.
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The Cellular Landscape of Alzheimer’s Disease: A Review of Repair, Response, and Resilience
Mark Sanchez
Neurodegeneration, like that seen in Alzheimer’s disease, is generally characterized by the progressive loss of cognitive function with widespread cellular dysfunction. Hallmark features of Alzheimer’s disease include the accumulation of amyloid -beta, tau pathology, DNA damage, and disruptions in glial cell function. Despite this knowledge, the mechanisms by which specific brain cell types contribute to or resist neurodegeneration remain unclear.
This review synthesizes findings from over twenty recent studies to explore how astrocytes and neurons respond to injury and chronic pathology, with special attention given to astrocytic vulnerability, DNA repair regulation, and the intersection between tau accumulation and chromatin structure. We organize our content around three central themes: cell type/location, cell-specific response dynamics, and cellular resiliency. In doing so, we examine how DNA repair pathways, oxidative stress defenses, and protein aggregation intersect to shape disease outcomes. Across each of these themes, we highlight discrepancies in results, identify gaps within the literature, and offer potential avenues for clarification of causal mechanisms and therapeutic targets. By framing the progression of neurodegenerative mechanisms through a cellular lens, we propose insights into new foundations for models of brain resilience and cellspecific therapeutics.
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The Coloniality of Gender and Class: Analyzing Maria Lugones Decolonial Feminism
Silvia Benavides
This paper examines Maria Lugones' theory of the colonial/modern gender system as a foundation for her framework of decolonial feminism. Lugones argues that because of colonialism, our society has gendered hierarchies. These hierarchies dehumanize Indigenous women and enforce European heterosexual norms. Drawing from Anibal Quijano’s concept of coloniality of power, Lugones expands the idea with the coloniality of gender. She emphasizes how race, gender, and sexuality are constructed by colonial violence. Lugones offers a powerful critique of traditional feminism’s Eurocentric bias. This paper aims to investigate the overlooked concept of class oppression to achieve a decolonial feminism. Applying the work of Iris Marion Young’s theory of structural injustice and the concept of the “Five Faces of Oppression, "specifically exploitation and powerlessness, this work shows how economic hierarchies intersect with colonial and gendered structures. A case study on women garment workers in Bangladesh is used to illustrate how global capitalism reinforces the gendered labor hierarchies that are rooted in colonialism and patriarchy. This expanded framework accounts for the lived experiences of marginalized women and offers a more inclusive approach to feminist resistance.
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The relationship of Phosphatidylethanol and Cholesterol in Non-Human Primates
Samantha Nguyen
Background: Phosphatidylethanol (PEth) is a metabolite of ethanol, therefore a direct detector of alcohol in the human body, which is used to identify and monitor severity of alcohol events.
There are standardized levels that indicate little to heavy drinking, ranging from 0-200+. PEth levels, however, are variable amongst humans. Non-human primates such as monkeys have been proven to produce PEth, being a viable model to test for these sources of variability amongst humans. In this study, we examined the relationship of cholesterol and lipid proteins to an abundant PEth homolog [16:0/18:01] in non-human primates existing in both humans and monkeys.
Methods: 6 monkeys were given unrestricted access to 4% ethanol solution and water 20 hours/day for 14 days. Whole blood samples were taken afterwards and then analyzed for Peth homolog [16:0/18:01] using high performance liquid chromatography (HPLC) as well as for total cholesterol, HDL, and LDL using an AF HDL & LDL/VLDL assay kit (ELISA).
Results: The results show that 1) PEth to lipid counts have a better accuracy when read immediately after alcohol consumption, compared to after 2 weeks; 2) the PEth/HDL had the highest correlation when reflected in volume intake (amount); 3) PEth/LDL had the highest correlation when reflected in concentration (dose).
Conclusion: Results suggest PEth to lipid ratios have a stronger correlation when blood is tested more recently compared to over time, as well as when PEth is compared to recent alcohol intake alone. More research needs to be conducted to better understand the relationship between phosphatidylethanol and lipid interaction.
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“Transgender Insanity” and Scapegoating: President Donald Trump’s Political Rhetoric Regarding Transgender Americans
Savannah Torres
Words are powerful. In an era where the words of political leaders evoke more attention than their policies, political rhetoric must be recognized for the powerful tool it is and its implications. The rhetoric of political leaders can greatly influence how a vulnerable community will be treated and viewed by society, with the choice language of the political leader signaling either inclusion or exclusion for the community. This study examines how President Donald Trump uses his political rhetoric to scapegoat the community of transgender Americans in the American political sphere. Limiting rhetorical media to speeches, interviews, remarks, rallies, and debates, transcribed by Roll Call, rhetoric mining is used to identify and analyze keywords, including “transgender”, “transitioned”, “sports”, “sex changes/operations”, “gender”, “hormone/puberty”, and “child sexual mutilation” to determine President Trump’s language pattern when referring to transgender Americans. Relying on J.L. Austin’s and John Searle’s speech act theory as a theoretical framework, this analysis explores how Trump’s political rhetoric functions as a performative act of scapegoating. With Excel being used to mine and analyze the repetition of the keywords, it is expected that the analysis will reveal that Trump’s performative act of scapegoating the transgender community can be seen through his purposeful stigmatizing language regarding transgender Americans, resulting in their political “otherness” and discrediting of their gender identity within the political sphere. Ultimately, this study seeks to raise awareness on how performative political rhetoric can contribute to the continued marginalization of vulnerable communities and emphasize the consequences of such rhetorical choices persisting.
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Turns Out, Latinx Faculty Doesn’t Mean the School Sucks: The Faux Relationship Between Latinx Saturation Rate and Institutional Prestige
Julianne Pena
Understanding Hispanic Serving Institutions (HIS) provides an avenue into shedding light on the lack of Latinx faculty within HSIs. The purpose of this study was to evaluate the relationship between saturation rate and prestige. Data were extracted from the Hispanic Association of Colleges and Universities to identify the HSIs and Integrated Postsecondary Education Data System to assess the total number of faculty, students, applicants, and those admitted to each institution. Results show that there is no relationship between White representation and institutional prestige.
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Why Aren’t Young Adults Voting? A Quantitative Analysis
Haley Aleman
Even though college aged students are one of the largest elibible voting blocks within the United States, students have in the past demonstrated low voter turnout in elections. This study aims to explore what the underlying causes of this phenomenon are through asking: Why are college aged students not voting? Through this, it is specifically investigated if voter disengagement stems from a sense of political apathy or rather from issues regarding accessibility. Using data from the American Nation Election Studies (ANES) database, responses from individuals aged 18 to 26 are analyzed in order to assess the relatiuonship between different barriers that may be contributing to a lack of electoral participation. To determine whether there are statistically significant relationships present between these multifaceted factors and the voting behavior of young adults, chi-square tests of independence will be employed. The primary goals of this study is to clarify is the present disengagement and offer a more nuanced understanding of voting behavior of young college aged individuals. Findings from this statistical analysis aim to inform future outreach and civic engagement promotion among universities to better support and increase participation in young voters.
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Analyzing the Relationships between Substance Abuse and Mental Health Disorders in American Adolescents, aged 12 to 25, Using a Quantitative, Correlational Design
Jacob Rodriguez
There have been series of articles which analyze the relationships between substance abuse and mental health disorders among. Many of these resources provide statistics which display the impact that substance abuse can have on mental health, and vice versa. Moreover, among the population of substance users, a wide fraction of them are adolescents. Adolescents who suffer from substance abuse and other mental health disorders generally come from low- income households or impoverished communities, poor quality education or have low educational achievement, and are unemployed or have low-paying jobs. A high percentage of these individuals are also white, with only a fraction of them being Hispanic, black, or other. Additionally, both males and females appear to be significantly affected by the impact of substance abuse in their lives. This research examines the relationship between substance abuse disorders and mental health disorders in a national sample of adolescents, ages 12-25. The research was conducted using a quantitative, correlational design. The results indicated multiple significant positive and negative correlations between substance abuse and mental health concerns. The results suggest the need for greater advocacy against substance abuse in adolescent populations and increased treatment to prevent its negative consequences. In this way, people can understand the devastating effects of substance abuse and initiate recovery, which would create a future that is populated with healthy emerging adults.
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Comparative Financial Analysis of HSIs, HBCUs, and Liberal Arts Colleges: Net Present Value and Return on Investment Perspectives
Sophia Phelan
This research presents a preliminary expedition into identifying the relationship between Net Present Value (NPV) and Return on Investment (ROI) of general liberal arts institutions, Hispanic Serving Institutions (HSIs), and Historically Black Colleges and Universities (HBCUs). HSIs and HBCUs have a long tradition of serving specific underrepresented communities of color, fostering success through culturally appropriate learning environments. This research utilizes a unique business lens to discuss the value of a service-based institution using tools such as value proposition and consumer fit. And then, it compares each set of institutions using ROI and NPV. The data, sourced from College Scorecard, Georgetown University, and Data USA, consolidated into a unique dataset composed of four-year universities that predominantly grant bachelor’s degrees in non-rural Texas, with a small (642) to small-medium (4,547) student body, and coincidentally, religiously affiliated. An important note is that on average the black student body population in this data set, as well as across Texas, tend to be smaller than the Hispanic student population which could affect the data and the overall performance of HBCUs. The preliminary outcomes of this research reveals concerning trends. While HSIs and liberal arts institutions exhibit similar NPVs over a 40-year period, HBCUs start from a lower NPV and experience slower growth. This disparity suggests a potential financial disadvantage for HBCU graduates. Similarly, HBCUs have lower ROIs, even when compared by majors to the other institutions. This research demonstrates a disparity in post-graduation achievement, in metrics of NPV and ROI, but further research is necessary to determine where the gap originates.
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Comparing three methods to determine acidity in brewed coffee
Carolina Saldivar
Coffee is a mixture made up of nonvolatile and volatile compounds which have a sensory appeal. The purpose of this study is to compare three methods to measure coffee acidity in a brewed coffee sample. The three methods are titratable acidity to a pH 8, a titration curve with a gran plot, and HPLC to detect 3-,4-,5-caffeoylquinic acids. The acidity measured using each method was 71 ± 9 mg/g, 45 ± 3 mg/g , and 27 ±6 mg/g, respectively, where the acidity is reported as mg CQA per gram of coffee grounds. HPLC resulted in the lowed amount of CQAs because only CQAs were being analyzed, while the other methods account for all acids in the coffee sample.
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Comprehensive Signal Strength Mapping for Indoor Object Localization
Joshua Riojas
Congested environments resulting in numerous reflections from one or more radio frequency (RF) sources exacerbate the accuracy of Time Space Positioning Information (TSPI). The St. Mary’s Unmanned Aerial Systems (UAS) Lab, being a highly reflective building (almost entirely metal), renders the use of GPS signals for indoor localization impractical. Consequently, this has led to exploring the utilization of RF reflections to determine an object’s position. Recently, Kimberly Tse, a graduate student from St. Mary’s University, designed a Convolutional Neural Network (CNN)-based TSPI localization model, achieving a 94% accuracy with synthetic data simulated via MATLAB and validated by real-world signal strengths gathered across a small area of the UAS Lab [1]. This paper presents a different approach to gathering signal strengths across the UAS Lab to provide comprehensive data for enhancing the machine learning model’s localization accuracy. We utilized a calibrated in- frared camera system with real-time TSPI to gather accurate positioning truth data and employed robotic cars to cover a specified area, thereby laying the groundwork for future analysis and model training with submillimeter precision.
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Computational Magnetic Reconnection in Plasma
Joshua Mendez
Magnetic reconnection is a fundamental process in plasma physics, playing a critical role in the dynamics of high-energy environments such as those found around pulsars. This research aims to elucidate how magnetic reconnection contributes to the regular high-energy electromagnetic emissions from pulsars through 3D computational modeling. Utilizing WarpX, an advanced software optimized for supercomputer clusters, we simulate plasma under extreme conditions to provide insights into the complex interactions and energy transformations. Our findings indicate that 3D models offer a more accurate representation of plasma behavior compared to 2D models, revealing significant details about the mechanisms driving pulsar emissions. This study advances our understanding of pulsar environments and the broader implications of magnetic reconnection in astrophysical plasmas.
