UTSA Runner Research Press (Univ. of Texas at San Antonio)
Not a member yet
6846 research outputs found
Sort by
Geochemical and Microbiological Factors Controlling the Mobility of Phosphorus in Soils From Prairie Grassland and an Agricultural Land in Kansas
Get PDFPhosphorus (P) is a fundamental nutrient for plant growth and a key component in agricultural productivity. However, its bioavailability in soils is often limited due to strong fixation with mineral surfaces and organic matter. In the context of rising global food demands and environmental concerns related to overfertilization, understanding the mechanisms governing soil P cycling is essential for sustainable nutrient management. This thesis investigates the soil phosphorus cycle across different land-use systems in Kansas, focusing on prairie grasslands and agricultural fields. The study integrates soil chemistry, organic matter composition, and microbial ecology to develop a comprehensive understanding of the factors influencing P mobilization and availability. The first part of this research examines the role of soil physicochemical characteristics and organic matter in determining bioavailable phosphorus. Through detailed geochemical analyses, it highlights how variables such as pH, soil texture, and the quality of soil organic matter influence phosphorus retention and release. The second part of the study explores the composition and diversity of soil microbial communities using 16S rRNA gene sequencing. It establishes clear differences in microbial abundance, richness, and phylogenetic diversity between land uses. These microbial patterns are significantly shaped by edaphic variables, including organic matter and labile P pools. Building on these findings, the third section focuses on phosphate solubilizing bacteria (PSBs) as functional mediators of soil P availability. It evaluates the relative abundance of potential PSB taxa, their associations with soil P levels, and the mechanisms through which they contribute to P solubilization, particularly in relation to soil organic matter dynamics. The thesis concludes by identifying critical research gaps, including the need for phosphorus fractionation, microbial enzymatic profiling, isotopic tracing of P sources, and controlled inoculation trials. It also highlights future directions for environmental policy and agricultural practices, such as integrating PSB-based biofertilizers and refining land-use-specific phosphorus management tools. Altogether, this research contributes valuable insights into the interactions between soil chemistry, microbial life, and land use in regulating phosphorus availability, offering a scientific basis for improved soil fertility management and sustainable agriculture.Earth and Planetary Science
Role of DNA Methylation in a Hispanic Childhood Obesity Cohort
Get PDFRecords within the US have shown that obesity remains a constant health issue throughout all stages of life with prevalence remaining elevated. Obesity is a complex disease, involving multiple factors both genetic and environmental. Epigenetics allows the study of the intersection of these two fields. Hispanics have higher prevalence of obesity, and associated diseases such as metabolic syndrome and diabetes. Despite this, they remain an understudied population both genetically and epigenetically. As such, there is a need to generate gene expression and epigenetic data in Hispanic cohorts, including profiling DNA methylation levels. While studying obesity-relevant tissue, such as muscle or adipose tissue, is ideal, this is hard to obtain. Therefore, interrogating DNA methylation correlations between functional and easy to obtain peripheral tissues is necessary. This project aims to address these gaps in knowledge by utilizing Hispanic cohorts to determine CpG loci associated with body mass index and percent fat, evaluate correlation of CpG loci between blood and muscle, and analyze the relationship between DNA methylation and gene expression within these tissues. We found CpG loci associated with BMI and percent fat in a Hispanic childhood cohort, and we established a subset of CpG loci correlated between tissues in a Hispanic adult cohort. Finally, we analyzed differential gene expression data, determining pathways enriched in differential expression as well as identifying cis-regulatory interactions between CpG loci and gene expression. These results may serve as a resource for further interrogation risk-factors and targets for understanding the mechanism behind obesity in the Hispanic population.Neuroscience, Developmental and Regenerative Biolog
The Impact of Affect Labeling and Narrative Writing on Distress in Response to the Trauma Film Paradigm
Get PDFPost-traumatic stress disorder (PTSD) is a debilitating mental health condition among college students and can lead to difficulties with school and home life. While there are empirically established interventions to treat PTSD, there is a need to explore how interventions can be modified to be more efficacious. Narrative writing is a brief intervention used in the treatment of PTSD symptoms and has been used in experiments with participants who endorse PTSD symptoms. In narrative writing, participants write about traumatic and/or stressful events that they have experienced. Studies demonstrate that narrative writing is efficacious and lessens PTSD symptom severity. Interestingly, individuals who use more feeling and emotion-based words (FEBW) in their writing samples tend to have better health outcomes than those who use fewer. The purpose of this study was to examine how training in affect labeling influences narrative writing to, in turn, influence levels of distress following the viewing of a standardized traumatic film scene. Using a randomized experimental design, college students (N = 62) participated in one of two conditions (n = 31 per condition): 1) a brief affect labeling session that provided psychoeducation of feelings and emotions (Affect +NW); and 2) a control group that did not engage in any sort of training (no training; NW). Both groups then viewed a standardized traumatic film scene, after which they were asked to engage in a narrative writing session. Distress was measured pre- and post-TFP, following the writing session, and then at five-, 15-, and 30-minute timepoints. The hypotheses were as follows: H1) Participants in the Affect + NW condition would have greater decreases in distress from pre- to post-writing compared to those in the control condition; H2) Participants in the Affect + NW condition would have distress dissipate at a faster rate over time points post-writing, five-, 15-, and 30-minute compared to those in the control condition; H3) Participants who used more positive affect related words would have less distress from pre- to post-writing and all other timepoints, regardless of condition; H4) Participants who used more negative affect related words would have higher levels of distress from pre- to post-writing but will have less distress at 30-minute follow up compared to those who do not use as many negative affect related words. The exploratory aims were as follows: 1) Examine the correlation between distress and specific word categories like causal and insightful words; 2) Examine how the affect labeling intervention impacted the relationship of alexithymia symptom severity on FEBW.
A manipulation check showed that the novel affect labeling training did result in participants using more FEBW than the no training condition. In addition, the film clip caused a significant increase in distress. Using a between-within subjects ANOVA, the impact of FEBW did not significantly impact distress levels between the two groups. Using a linear mixed model (LMM), no significant results were found when looking at the effect of positive affect and negative affect related words on distress. Further, an LMM did not yield significant findings when examining causal and insight words on distress. Lastly, a moderation analysis concluded that while there was a significant relationship between alexithymia symptom severity and use of FEBW, the relationship between the two was not significantly impacted by the affect training. While findings of the present study were not significant, the decreases in distress between both groups provides support for the continued use of narrative writing in response to negative stimuli. Implications and future consideration are discussed.Psycholog
Tensor-Based Uniform and Discrete Multi-View Projection Clustering
No full textMulti-view graph clustering (MVGC) utilizes affinity graphs to efficiently obtain information between views. Although various excellent MVGC methods have been proposed, they still have many limitations. To surmount these limitations, this work develops a novel tensor-based unified and discrete multi-view projection clustering (TUDMPC) approach. Specifically, TUDMPC uses projection and the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>L</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></semantics></math></inline-formula>-norm for feature selection to reduce the effects of redundancy and noise. Meanwhile, the differences among similar graphs are minimized through the tensor kernel norm to better leverage information across views and capture high-order correlations. In addition, the rank constraint is applied to keep the affinity graphs with a discrete cluster structure, and the clustering results are obtained directly in a unified joint framework. Finally, an efficient optimization algorithm is proposed to obtain the clustering results. Experiments are conducted to compare the clustering results of TUDMPC with seven baseline methods. The results show that TUDMPC outperforms the existing methods.Busines
Incivility or Invalidity? Evaluating Perspective API Scores as a Measure of Political Incivility
Get PDFAdopting toxicity scores produced by Google’s Perspective API has become perhaps the most popular approach to estimating the prevalence of incivility and toxicity in online communication. However, existing scholarship has often overlooked the imbalanced nature of incivility when evaluating the performance of Perspective scores, leading to an overestimation of their effectiveness. In this research note, we demonstrate that once this imbalance is considered, Perspective scores perform poorly in accurately identifying incivility. We then propose a method that leverages Perspective scores to reduce the manual coding needed for developing a high-performing supervised learning classification model. Our findings indicate that combining Perspective scores with supervised learning approaches yields significantly better results for identifying incivility.Political Science and Geograph
How Architecture Builds Intelligence: Lessons from AI
No full textThe architecture in the title refers to physical buildings, spaces, and walls. Dominant architectural culture prefers minimalist environments that contradict the information setting needed for the infant brain to develop. Much of world architecture after World War II is therefore unsuitable for raising children. Data collected by technological tools, including those that use AI for processing signals, indicate a basic misfit between cognition and design. Results from the way AI software works in general, together with mobile robotics and neuroscience, back up this conclusion. There exists a critical research gap: the systematic investigation of how the geometry of the built environment influences cognitive development and human neurophysiology. While previous studies have explored environmental effects on health (other than from pathogens and pollutants), they largely focus on factors such as acoustics, color, and light, neglecting the fundamental role of spatial geometry. Geometrical features in the ancestral setting shaped neural circuits that determine human cognition and intelligence. However, the contemporary built environment consisting of raw concrete, plate glass, and exposed steel sharply contrasts with natural geometries. Traditional and vernacular architectures are appropriate for life, whereas new buildings and urban spaces adapt to human biology and are better for raising children only if they follow living geometry, which represents natural patterns such as fractals and nested symmetries. This study provides a novel, evidence-based framework for adaptive and empathetic architectural design.Mathematic
Artificial Intelligence-Driven Optimal Charging Strategy for Electric Vehicles and Impacts on Electric Power Grid
No full textElectric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the &ldquo;duck curve&rdquo;. Data from Caltech&rsquo;s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework&rsquo;s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs.Electrical and Computer Engineerin
Applied Color Vision in Filtered Spaces: Integrating Perception, Cognition, and Decision-Making in Search Detection Tasks
Get PDFThere are many situations in which we view the world through a filter. Psychophysical research on perceiving information tends to focus on information lost directly due to the filter. However, many interactions with the world depend on relationships among information sources. This study investigated the effects of colored filters on an individual’s visual field and its influence on the perception of target cues and how that information is processed for choice responses. A simple search detection task was conducted with 20 participants. Target stimuli varied in condition between control and color filtered symbols. Analyses showed a correlation between color distance, accuracy, and RTs – showcasing a speed-accuracy tradeoff for task completion. Color perception plays a vital role in how an individual accumulates and processes information from their visual field. Alterations to the normal visual scene, caused by colored filters, can impact cognitive load and decision-making.Psycholog
Effect of Microstructural Changes on the Magnetization Dynamics Mechanisms in Ferrofluids Subjected to Alternating Magnetic Fields
Get PDFWe investigated the effects of chemical and physical changes on the interplay between the N&eacute;el and Brown superspin relaxation mechanisms in ferrofluids containing 18 nm-diameter Co<sub>0.2</sub>Fe<sub>2.8</sub>O<sub>4</sub> magnetic nanoparticles. We attempted to tune the ferrofluid&rsquo;s magnetization dynamics via three methods: (i) changing the carrier fluid from Isopar M to kerosene (ii) doubling the Co-doping level from x = 0.2 to x = 0.4, and (iii) diluting the Co<sub>0.2</sub>Fe<sub>2.8</sub>O<sub>4</sub>/Isopar M nanomagnetic fluid from &delta; = 1 mg/mL to &delta; = 0.1 mg/mL. We used temperature-resolved ac-susceptibility measurements at different frequencies, &chi;&Prime; vs. T|<sub>f</sub>, to gain insight into the thermally driven superspin dynamics of the nanoparticles within the ferrofluid. Our data demonstrates that both increasing x and using a different carrier fluid quantitatively alter the temperature dependence of the N&eacute;el and Brown relaxation frequency (f<sub>N</sub> vs. T and f<sub>B</sub> vs. T) by changing the nanoparticles&rsquo; magnetic moments and the fluid&rsquo;s viscosity. Yet, the two mechanisms remain decoupled, as indicated by the presence of two magnetic events (peaks in the &chi;&Prime; vs. T|<sub>f</sub> datasets) one corresponding to the N&eacute;el and the other to Brown relaxation. On the other hand, diluting the ferrofluid leads to a qualitative change in the collective superspin dynamics behavior. Indeed, there is just one &chi;&Prime;-peak in the data from the &delta; = 0.1 mg/mL nanofluid, and its f vs. T dependence is well-described by a model that includes coupled contributions from both the N&eacute;el and Brown relaxation: <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="normal">f</mi><mfenced separators="|"><mrow><mi mathvariant="normal">T</mi></mrow></mfenced><mo>=</mo><mi mathvariant="normal">p</mi><mo>&middot;</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><mi>T</mi></mrow><mrow><msub><mrow><mi>&gamma;</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></mfrac></mstyle><mo>&middot;</mo><mi mathvariant="normal">e</mi><mi mathvariant="normal">x</mi><mi mathvariant="normal">p</mi><mfenced open="[" close="]" separators="|"><mrow><mo>&minus;</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><msup><mrow><mi mathvariant="normal">E</mi></mrow><mrow><mo>&prime;</mo></mrow></msup></mrow><mrow><msub><mrow><mi mathvariant="normal">k</mi></mrow><mrow><mi mathvariant="normal">B</mi></mrow></msub><mfenced separators="|"><mrow><mi mathvariant="normal">T</mi><mo>&minus;</mo><msubsup><mrow><mi mathvariant="normal">T</mi></mrow><mn>0</mn><mo>&prime;</mo></msubsup></mrow></mfenced></mrow></mfrac></mstyle></mrow></mfenced><mo>+</mo><mo>&nbsp;</mo></mrow></semantics></math></inline-formula> (1 &minus; p) <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">f</mi></mrow><mrow><mn>0</mn></mrow></msub><mi mathvariant="normal">e</mi><mi mathvariant="normal">x</mi><mi mathvariant="normal">p</mi><mfenced open="[" close="]" separators="|"><mrow><mo>&minus;</mo><mstyle scriptlevel="0" displaystyle="true"><mfrac><mrow><msub><mrow><mi mathvariant="normal">E</mi></mrow><mrow><mi mathvariant="normal">B</mi></mrow></msub></mrow><mrow><msub><mrow><mi mathvariant="normal">k</mi></mrow><mrow><mi mathvariant="normal">B</mi></mrow></msub><mfenced separators="|"><mrow><mi mathvariant="normal">T</mi><mo>&minus;</mo><msub><mrow><mi mathvariant="normal">T</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow></mfenced></mrow></mfrac></mstyle></mrow></mfenced></mrow></semantics></math></inline-formula>. This is a remarkable behavior that demonstrates the ability to control a ferrofluids magnetization dynamics through simple chemical and physical changes.Physics and Astronom
Advancing Workplace Safety Through Intelligent Monitoring: Applications of Artificial Intelligence and Computer Vision in High-risk Industrial Environments
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 August 26, 2027.High-risk industrial environments have continued to experience elevated workplace injuries, in part because traditional manual safety assessments still face major drawbacks related to subjectivity, limited scalability, and difficulty adapting to complex and changing environmental contexts. The primary objective of this research is to systematically investigate and address critical gaps in feasibility, scalability, contextual enhancement, and adaptive intelligence for computer vision-based safety and ergonomic risk assessment across high-risk industrial environments. The research methodology follows a systematic four-phase process, each contributing critically to the overall objective. Phase one establishes computer vision feasibility for personal protective equipment detection in steel manufacturing environments using labeled image datasets and cross-validation techniques. Phase two develops an integrated unmanned aerial vehicle (UAV)-based computer vision framework using pose estimation algorithms for automated ergonomic risk assessment, validated through construction site deployment. Phase three creates the Elevated Construction Ergonomic Risk Index (ECERI) using multi-tier validation methodology, including theoretical proofs, computational simulations, and empirical expert judgment comparisons to enhance traditional Rapid Entire Body Assessment (REBA) with environmental context factors. Phase four implements the Self-Organizing Fuzzy Inference System (SOFIS) incorporating dual uncertainty quantification through information-theoretic frameworks and Monte Carlo methods with expert-in-the-loop validation. The research demonstrates successful computer vision implementation in challenging industrial environments, validates scalable UAV frameworks for comprehensive ergonomic monitoring, establishes context-aware assessment methods improving traditional approaches, and creates adaptive intelligent systems with uncertainty awareness and continuous learning capabilities. These findings provide validated solutions addressing each identified gap in current safety monitoring approaches, contributing practical tools and theoretical advancement for improved occupational safety monitoring systems across high-risk industries.Civil and Environmental Engineerin