UTSA Runner Research Press (Univ. of Texas at San Antonio)
Not a member yet
6846 research outputs found
Sort by
Fabrication and Characterization of Chitosan Functionalized Nanowire Arrays for the Non-Enzymatic Detection of Glucose
No full textDiabetes remains one of the leading causes of death in the United States and costs patients approximately $17,000 dollars annually in medical expenditures, a large portion of which goes towards their blood glucose monitoring supplies. To reduce the burdens caused by diabetes, nanomaterials have been explored as accurate and more affordable alternative glucose sensors. The use of these nanomaterials as potential non-enzymatic glucose sensors has been widely explored to replace traditional enzymatic glucose sensors, which utilize glucose oxidase as the sensing agent for detecting glucose. Although the gold standard of glucose detection for many years, glucose oxidase is expensive, degrades in hot and humid conditions, and has been shown to give inconsistent readings across brands of test strips. Therefore, the goal of this work was to successfully develop a method for the fabrication of nickel nanowire arrays coated with a thin layer of chitosan for the non-enzymatic detection of glucose, and then test the sensor?s ability to detect glucose in ideal and physiologically accurate samples. Structural characterization via electron microscopy and Raman spectroscopy showed the high order and uniformity of the nickel nanowire arrays as well as confirmed the presence of the thin layer of chitosan. Electrochemical testing via cyclic voltammetry and chronoamperometry revealed the chitosan coating improved the stability of the electrochemical sensor during glucose detection and exposure to interfering species, improved the sensitivity of the sensor by 46.39 %, and increased the linear range of the sensor from 3.846 mM to 4.372 mM. Additionally, the chitosan coating not only increased the linear detection range, but also demonstrated selectivity after exposure to physiologically accurate samples and was able to prevent biofouling after exposure to proteins.
In summary, the chitosan layer slowed down the biofouling process during glucose detection while improving the glucose detection capabilities of the nickel nanowire array sensor. Furthermore, the chitosan?s anti-biofouling properties allowed the sensor to remain functional when exposed to a complex and biologically accurate solution, demonstrating the potential for the sensor to be used with real life samples. Overall, this work highlights the role chitosan plays in the non-enzymatic detection of glucose, helping to pave the way for the future of glucose detection and monitoring.Physics and Astronom
Computer Vision for Safety Management in the Steel Industry
No full textThe complex nature of the steel manufacturing environment, characterized by different types of hazards from materials and large machinery, makes the need for objective and automated monitoring very critical to replace the traditional methods, which are manual and subjective. This study explores the feasibility of implementing computer vision for safety management in steel manufacturing, with a case study implementation for automated hard hat detection. The research combines hazard characterization, technology assessment, and a pilot case study. First, a comprehensive review of steel manufacturing hazards was conducted, followed by the application of TOPSIS, a multi-criteria decision analysis method, to select a candidate computer vision system from eight commercially available systems. This pilot study evaluated YOLOv5m, YOLOv8m, and YOLOv9c models on 703 grayscale images from a steel mini-mill, assessing performance through precision, recall, F1-score, mAP, specificity, and AUC metrics. Results showed high overall accuracy in hard hat detection, with YOLOv9c slightly outperforming others, particularly in detecting safety violations. Challenges emerged in handling class imbalance and accurately identifying absent hard hats, especially given grayscale imagery limitations. Despite these challenges, this study affirms the feasibility of computer vision-based safety management in steel manufacturing, providing a foundation for future automated safety monitoring systems. Findings underscore the need for larger, diverse datasets and advanced techniques to address industry-specific complexities, paving the way for enhanced workplace safety in challenging industrial environments.Civil and Environmental Engineering, and Construction Managemen
Influence of land use on geochemistry and microbial population and their subsequent effects on soil phosphate mobilization across a climatic transect in Kansas
No full textIntroduction:
➢ Phosphorus(P) is one of the major nutrients required for plant growth and it is important to understand its dynamics in soils and identify the factors that govern its cycling in nature
➢ Understanding abiotic and biotic drivers of P across land use will broaden our knowledge of soil P cycling in semi-arid ecosystems
Methods:
➢ Samples collected from two regions – Konza Prairie LTER and Hays, which lie on a climatic transect across Kansas
➢ These two locations have contrasting land use patterns: Konza Prairie(LTER) - naturally pristine site with minimal human intervention, Hays - agriculturally influenced site
Konza Prairie soil type: Sandy loam
Hays soil type : Loamy soil
Sample collection:
Soil samples collected from depths 0-6” inches from the surface from different watersheds in Konza (n=18) and from different land use areas in Hays (n=12) followed by preservation in anaerobags using O2 absorbers and subsequent storage in 4°CEnvironmental Science and Engineerin
Examining the Role Communication Plays in Negotiating Athletic Identity in Relation to Mental Health and Stigma
No full textThis study examines the intricate dynamics of athletic identity negotiation and mental health stigma among collegiate student-athletes. Through the exploration of three research questions, the study investigates how student-athletes navigate their athletic identity in relation to their team and mental health, negotiate the stigmas surrounding mental health, and propose actions to improve mental health literacy and treatment utilization. Drawing from the Communication Theory of Identity (CTI) and utilizing Stigma Management Communication Theory (SMC), Uncertainty Reduction Theory (URT), and Disclosure Decision-Making Model (DD-MM), the research examines the multifaceted aspects of identity and stigma. Findings reveal the influence of social dimensions on athletic identity formation, highlighting the role of shared experiences and societal expectations. While acknowledging the complexity of addressing mental health in collegiate athletics, the study underscores the importance of holistic approaches that prioritize athlete-centric interventions. By amplifying student-athletes' voices and integrating evidence-based strategies, institutions can foster a supportive environment conducive to mental well-being.Communicatio
Technical Report, No. 109
No full textIn March 2024, the Center for Archaeological Research (CAR) at the University of Texas at San Antonio (UTSA) conducted an intensive pedestrian survey of the proposed Culebra-Helotes Creeks Connector Trail project in northwest San Antonio, Bexar County, Texas. The work was conducted in response to a request from Adams Environmental, Inc. (AEI), which is an environmental subcontractor of the San Antonio River Authority (SARA). The project area is on land owned by the City of San Antonio (COSA), a political subdivision of Texas. As such, the project requires regulatory review by the Texas Historical
Commission (THC) under the Antiquities Code of Texas (Texas Natural Resources Code, Title, Chapter 191) and by the COSA Office of Historic Preservation (OHP) under the Unified Development Code (UDC; Article 6 35-630 to 35-634). The trail was realigned following the initial fieldwork, CAR requested a permit amendment which was approved in early July 2024 by THC to investigate areas outside the original project area. Cynthia Munoz, CAR Interim Director, served as the Principal Investigator for the project under Texas Antiquities Permit Number 31640. Leonard Kemp served as the Project Archaeologist.
CAR archaeologists excavated 18 of 31 planned shovel tests within the 2.9 km length of the project area. The shovel tests terminated at an average depth of 33 cm below surface (cmbs). The excavation revealed shallow soils with abundant gravels and cobbles. Shovel tests were not excavated in the western portion of the project area due to the presence of surface bedrock and flood control construction. None of the excavated shovel tests contained archaeological materials or features. No artifacts or features were documented on the surface. Due to the lack of intact soil and presence of gravels and bedrock throughout the project area, CAR recommends that mechanical trenching is not warranted. CAR also recommends that construction of the connector trail proceed as planned. All records generated during the course of this project are permanently curated at the CAR under accession number 2864.Adams Environmental, Inc
Hispanic Health in the United States: Duration of Residence, Social Capital, and New Destinations
No full textHispanics are the fastest-growing racial/ethnic subgroup in the United States. Lower rates of educational attainment and higher rates of chronic disease are characteristics of the Hispanic population, yet on average Hispanics live longer than non-Hispanic Whites.
The purpose of this research is to further examine features of the Hispanic paradox and capture different aspects of the Hispanic health experience in the United States. The following address different topics related to Hispanic health: Duration of Residence, Social Capital, and New Destinations.
In the first chapter we found more evidence of a Hispanic mortality advantage, even among obese Hispanics. We also saw that among Hispanics, the improved mortality risk appears to attenuate the more time spent in the United States.
For the second chapter, Hispanics seem to have lower levels of inflammation or stress response biomarkers compared to non-Hispanic Whites and non-Hispanic Blacks. Additionally, adolescent indicators of social capital appeared to not have a protective effect on inflammation levels.
Lastly, counties with a larger Hispanic presence tended to have lower rates of obesity compared to those where Hispanics make up a lesser proportion of the population. Additionally, Hispanic new destination counties had lower rates of obesity compared to established Hispanic destination hubs.Applied Demograph
Through the Rainbow Labyrinth: A Narrative Inquiry of Queer Women Leadership in Collegiate Recreation
No full textThis study examined the unique leadership experiences of queer women in collegiate recreation, focusing on joy as a critical aspect of their professional journey. Employing a narrative inquiry methodology, the research documented the lived experiences of queer women in executive-level roles, exploring the complex intersection of gender, sexuality, and leadership within a traditionally male-dominated field. The study was framed by queer theory, leadership labyrinth theory, and intersectionality, offering a nuanced perspective that centered on both the challenges and moments of joy these women encountered. Findings revealed that queer women leaders navigated a "rainbow labyrinth" marked by resilience and a commitment to authenticity amidst heteronormative and cisnormative pressures. These leaders confronted stereotypes and biases while contributing to a more inclusive environment in collegiate recreation. The intentional focus on joy not only challenged deficit-based views but also highlighted the strength and transformative potential within queer identities. The study contributed to the limited scholarship on queer women?s leadership in higher education, emphasizing the significance of identity, agency, and community-building in creating spaces for both personal and professional fulfillment.Educational Leadership and Policy Studie
Robustness and Dependability of Deep Learning Models for Real-World Applications
No full textRobustness and dependability of Deep Learning (DL) models are critical for real-world DL-based applications. In this dissertation, we explore real-world threats affecting three DL-based applications: a smartphone-powered, computer-vision-based system for Power Wheelchair Intelligent Assistive Driving (PWC IA-Driving), a gene expression-based Deep Neural Network (DNN) for cancer-type prediction, and an efficient and intelligent attack detection in Software Defined IoT Networks. Subsequently, we propose methodologies to enhance the robustness and dependability of these applications.
Firstly, we develop the PWC IA-Driving system, aiming to enable the safe, hands-free operation of a Power Wheelchair (PWC) with reduced user attention required in indoor environments. This system alleviates the burden on disabled individuals and reduces their stress. Our goal is to offer an affordable and practical solution that can be seamlessly integrated into existing PWCs. The system utilizes a customized and pre-trained ResNet-based model on a smartphone to interpret driving commands from real-time imagery captured by the phone's camera. These instructions are then transmitted to the PWC via a control interface connected to the smartphone. We have developed a prototype of this assistive driving system on a Pixel-6 Android phone and tested its feasibility on a mobile robot as a proof-of-concept.
To ensure the mobile robot can navigate safely at reasonable speeds with minimal user intervention, we employ various techniques to enhance robustness and dependability. These include model explanation to increase confidence, data augmentation to improve accuracy for unseen scenarios, model distillation and quantization to enhance robustness against possible adversarial attacks, and utilization of on-device acceleration devices to improve response time and tolerance to low-confidence predictions, among others.
Secondly, we delve into the threats posed by adversarial attacks on Deep Neural Networks (DNNs), where adversaries can manipulate DNN outputs by crafting small, carefully designed perturbations to the inputs. These attacks present significant challenges to the practical deployment of DNNs. In this dissertation, we investigate a variety of defense methods against adversarial attacks on gene expression-based DNNs for cancer-type prediction. We propose a novel method called "segment patching" to mitigate the effects of adversarial perturbations. Segment patching effectively replaces perturbed input data segments with clean segments from the training dataset based on Euclidean distance. Our experiments demonstrate that this method maintains model prediction accuracy against adversarial attacks, particularly strong attacks. More importantly, the segment patching method poses a significant challenge to adversaries attempting to generate adversarial examples. Additionally, we explore the application of Fast Fourier Transform to transform input data into the frequency domain before feeding it into the DNN model. This approach aims to further obscure model gradients, making gradient-based attacks more difficult. Our findings suggest promising strategies for enhancing DNN robustness against adversarial attacks.
Thirdly, we explore the dependability issues surrounding Deep-learning based abnormality detection for IoT coupled with Software Defined Network (SDN) technologies. With the increasing integration of IoT devices into various domains such as smart buildings and critical infrastructure protection, their limited capabilities pose significant security vulnerabilities, especially when coupled with SDN technologies to provide flexible services. In this study, we concentrate on Random Forest (RF) machine learning models and scrutinize the impact of different feature sets (e.g., IPs and ports) on the detection accuracy for various attacks. Our aim is to enhance dependability through two main approaches: firstly, evaluating the effects of RF configurations (specifically forest size and tree depth) on detection accuracy and runtime overheads to improve response time by reducing forest size; secondly, generating a substantial amount of trusted labeled data by simulating attacks within our SDN environment. Our findings indicate that RF demonstrates high detection accuracy with the selected feature sets across different attacks. Furthermore, even with reduced forest sizes (e.g., fewer trees or shallower depth), the detection accuracy of RF only experiences a slight decrease, allowing for significant reductions in runtime overheads and thereby enhancing response time.Electrical and Computer Engineerin
Understanding the Relationship Between Precipitation and Crash Dynamics on Texas Roadways
No full textThe full text of this item is not available at this time because the author has placed this item under an embargo until September 11, 2026.This dissertation addresses the critical issue of road safety under adverse weather conditions, specifically examining the impact of precipitation on traffic crashes across Texas. Using crash data from the Texas Department of Transportation's CRIS database and high-resolution radar rainfall data from the NEXRAD Doppler Radar system, the study is structured in two main phases: an overarching analysis of statewide data followed by a detailed examination of Bexar and Harris Counties from 2006 to 2021. In the initial phase, a comprehensive analysis of Relative Risk Factor (RRF) across Texas was conducted using matched-pair methodology. The findings reveal that precipitation significantly elevates crash risk, with an annual average RRF indicating a 38% increase in crash likelihood during rainy conditions. This phase highlights the profound influence of rainfall on crash frequency and severity, setting the foundation for targeted regional studies. The subsequent phase focuses on Bexar and Harris Counties. In Bexar County, the analysis shows a significant increase in crash risk and injury severity during heavy rainfall events, with peak RRF values during intense precipitation. Specific hotspots, particularly on high-speed roads and among young male drivers, were identified. January has the highest monthly RRF (2.14), and the early morning hours between 3:00 AM and 6:00 AM show the highest hourly RRF (2.73). In Harris County, RRF calculations consistently indicate increased crash risks during wet conditions, with significant demographic and temporal variations. Young male drivers remain the most vulnerable, particularly during early morning hours and winter months. High-speed roads and entrance/exit ramps again show higher RRF values, underscoring critical areas for intervention. Overall, this dissertation offers essential insights for policymakers and urban planners to develop strategies enhancing transportation safety and sustainability by addressing human factors, vehicle safety technology, and road design to mitigate crash risks during precipitation events.Civil and Environmental Engineerin
Dealing with the social-emotional effects of the COVID-19 pandemic: School administrators’ leadership experiences in Texas, USA
No full textThe onset of COVID-19 in March 2020 presented unprecedented disruption to the education systems across the globe. Given that school leaders were at the forefront of guiding schools during the tumultuous times, the purpose of this article is to highlight the aftereffects of the COVID-19 pandemic on schools and examine how school leaders addressed these challenges, particularly the lingering social-emotional disruptions the students and teachers are experiencing. This qualitative study utilized an online survey to collect the perspectives of South Texas school leaders on the challenging circumstances of the COVID-19 pandemic. The findings are organized by: leadership experiences during the COVID-19 pandemic (lessons learned and critical practices), additional knowledge and skills (social-emotional well-being, resources to address social-emotional well-being, and parental engagement), and suggestions for preparation programs (students’ social-emotional learning (SEL) and teachers’ well-being). The scale of emerging pandemic-related challenges has left school leaders scrambling to seek innovative approaches to maintain a safe and orderly teaching and learning environment. Implementing SEL for students and educators seems to hold promise.Educational Leadership and Policy Studie