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Does the Time Spent with Same-Gender Peers Matter? Gender Segregation and Attitudes about Coercive Sex in College Students
No full textGender segregation, or the tendency to form friendships with same-gender peers, is a common phenomenon across the lifespan (Mehta & Strough, 2009). Research suggests that same-gender peer groups reinforce traditional gender norms (Leaper, 2022), which may contribute to coercive sexual attitudes, which are beliefs that justify sexual coercion. This study examined how different measures of gender segregation, peer nomination, actual time spent with same-gender peers, and ideal time spent with same-gender peers relate to coercive sexual attitudes. These attitudes were assessed through rape myth acceptance, which reflects beliefs that minimize or justify sexual violence (Canan et al., 2023), and sexual deception, which involves misleading behaviors in sexual interactions (Marelich et al., 2008). Participants (N = 343; 58.4% women; 78.9% White) between the ages of 18–29 (M = 19.97) completed an online survey. A 2 × 3 mixed-model MANOVA revealed a significant interaction, indicating that gender segregation varied by measurement type and gender. Men reported greater gender segregation than women in the actual time measure. Moderation analyses using PROCESS in SPSS examined whether gender segregation, gender, and their interaction were associated with coercive sexual attitudes. Results indicated that men reported greater rape myth acceptance, while women reported greater sexual deception. Two significant interaction effects emerged, the association between peer nomination and rape myth acceptance for men, and for women the association between actual time measurement and rape myth acceptance. These findings emphasize the importance of measurement in assessing gender segregation, as different methods revealed differing gendered patterns. By introducing actual time and ideal time measures, this study moves beyond traditional peer nomination, offering a more nuanced understanding of how same-gender peer interactions shape beliefs about sexual relationships. The results highlight the role of gendered socialization in reinforcing rape myth acceptance, underscoring the need for interventions that challenge traditional gender norms
Influence of Actuated Air Brakes on the Apogee of Sub-Orbital Rockets
No full textCollegiate engineering competitions have played a foundational role in the educational outcomes of university students for as long as they have existed. Among these competitions is the Spaceport America Cup, which challenges students to design, build, and launch high-power rockets with the incentive to fly as close as possible to a pre-established target apogee. Many teams competing in this competition have chosen to approach the challenge by incorporating deployable air brakes into their rocket designs. This approach comes with many uncertainties, both in controls and aerodynamics. For teams just starting out, aerodynamic performance is often one of the most challenging metrics to hone down, especially for teams lacking robust experimental and computational testing capabilities. This thesis aimed to alleviate some of these uncertainties with an aerodynamics-focused investigation of this design challenge. At the center of this investigation was the question of whether air brakes can functionally govern the apogee of a sub-orbital rocket platform. Additional interest was placed in evaluating the impact that air brakes have on the aerodynamic stability of a rocket platform and whether the drag characteristics of air brakes upheld any predictable consistencies. Computational fluid dynamics (CFD) and numerical simulations were used to carry out this investigation by way of aerodynamic and flight trajectory analyses on a generic rocket platform and four unique air brake configurations. The findings of this research showed a compelling ability for air brakes to successfully govern apogee and corroborated the findings and incentives of teams actively integrating this solution. It was also shown in review that aerodynamic stability was largely upheld for air brakes positioned aft of the center-of-gravity location. Finally, an evaluation of air brake performance proved to yield a drag force parameter that held consistent across brake designs and Mach number, and it is hoped that the consistency of this parameter will greatly assist teams in garnering a preliminary understanding of their designs’ aerodynamic performance
Reprogramming of immunometabolic functions in brain microvascular endothelial cells during neuroinflammation: insights from ischemic stroke and sepsis
No full textIschemic stroke and sepsis-associated encephalopathy (SAE) are two distinct conditions that share common pathophysiological mechanisms affecting brain microvascular endothelial cells (BMECs). Ischemic stroke results from an acute reduction in cerebral blood flow due to thrombosis or embolism, leading to energy failure, oxidative stress, and blood-brain barrier (BBB) dysfunction. SAE, a neurological complication of sepsis, arises from systemic inflammation, endothelial activation, and BBB disruption, leading to cognitive impairment and neuroinflammation. Despite their different etiologies, both conditions involve BMEC dysfunction as a central mediator of neurovascular injury. This dissertation aims to investigate the immunometabolic dysfunction of BMECs in ischemic stroke and sepsis, with a particular focus on the role of tissue-nonspecific alkaline phosphatase (TNAP) in regulating endothelial metabolism and inflammation. By elucidating BMEC-specific metabolic adaptations and inflammatory responses, this research seeks to identify potential therapeutic targets for mitigating neurovascular damage. Experimental models of ischemic stroke and sepsis were utilized to assess metabolic and bioenergetic changes in BMECs. Seahorse metabolic assays were employed to measure glycolysis and oxidative phosphorylation, while high-dimensional flow cytometry and metabolomic profiling were used to characterize immune and metabolic shifts. Transgenic mouse models with endothelial-specific deletion of TNAP were utilized to determine its role in immunometabolic regulation. Findings indicate that both ischemic stroke and SAE trigger BMEC metabolic reprogramming, shifting towards fatty acid metabolism with slight alternation in glycolysis and oxidative phosphorylation. TNAP was found to modulate endothelial metabolism by regulating fatty acid metabolism enzymes and metabolites and increased microglia and B cell migration to the site of inflammation. Loss of TNAP activity exacerbated BBB breakdown and neurovascular inflammation in ischemic stroke. Our results indicate that BMEC dysfunction serves as a critical driver of neurovascular injury in ischemic stroke and SAE. The immunometabolic interplay within these endothelial cells presents a novel target for therapeutic intervention. TNAP emerges as a key regulator of endothelial metabolism, and its modulation could provide a potential strategy for preserving BBB integrity and mitigating neuroinflammatory responses in ischemic and septic brain injuries
Refinement of a thermodynamic model to explain the weathering patterns of ignitable liquids on household substrates at elevated temperatures
No full textABSTRACT
Refinement of a thermodynamic model to explain the weathering patterns of ignitable liquids on household substrates at elevated temperatures
Chaney A. Ganninger
In 2021, arson caused more than 4,361 deaths in the United States. To aid in the differentiation between arson and accidental or natural causes, investigators often analyze fire debris for the presence of ignitable liquid residues using headspace concentration followed by gas chromatography-mass spectrometry (GC-MS). Classification of ignitable liquids can be confounded by weathering, which is the uneven evaporation of compounds in ignitable liquid residues. The Jackson group, among others, has developed a thermodynamic model to help understand the effects of substrates and elevated temperatures on the weathering of ignitable liquids. Previous work in the Jackson group was restricted to a simple artificial gasoline mixture to facilitate model development. The current research used commercial gasoline as the basis for experiments. Weathering of gasoline was conducted on different substrates at 210 °C, and traditional headspace concentration was compared with solvent extraction as alternative methods to extract and concentrate residues from the substrates. Headspace concentration of gasoline residues replicates the methods commonly used in laboratory casework. Modeling this data required several accommodations to the original thermodynamic model, including: 1) a calibration method to predict vapor pressures from retention indices, 2) retention time alignment to facilitate chromatographic comparisons between modeled and measured data, 3) an adjustment for a proportion of unobserved volatiles, 4) a correction factor to address the resistance to mass transfer of gasoline with different substrates, and 5) the effect of headspace concentration on the chromatographic abundances. The substrates included nylon carpet, laminate wood flooring, vinyl flooring, polyurethane foam, and drywall.
Experimental extents of weathering ranged from 0 to 99.9% (w/w) across all substrates. To achieve a final measurable volume after weathering of approximately 200 µL for all samples, different initial volumes ranged from 250 µL to 1000 µL. The extents of evaporation were determined by the mass of gasoline before and after evaporation. After the implementation of all changes, the thermodynamic model was able to predict the extent of weathering of gasoline using the total ion chromatograms (TICs). When residues were extracted using solvent extraction, the absolute error in the extent of weathering was approximately ±3%. This accuracy was achieved with Pearson product moment correlation (PPMC) values from 0.90-0.99 between the modeled and measured chromatograms. For gasoline residues extracted using headspace concentration, the model was less accurate at predicting the extent of evaporation. Our residue volumes were on the order of 200 mL, which exceeded the capacity of the activated charcoal strips and caused a strong bias against the more volatile components. Passive headspace concentration samples also provided larger random variance between modeled and measured chromatograms because of subtle differences in the extraction efficiencies of aromatic versus aliphatic compounds. The predictions of this thermodynamic model provide physical and chemical explanations for how and why gasoline weathered to 90% or more can still appear to be unweathered when using headspace concentration and analyzed using GC-MS. Liquid extractions of gasoline evaporated on drywall showed the counterintuitive result that volatiles can become enriched relative to fresh gasoline and therefore appear to be less weathered than the original liquid
Essays on Application of Machine Learning to Financial Statement Fraud
No full textThis dissertation is composed of two studies, both of which revolve around the application of machine learning to financial statement fraud. The first study entitled, “Complementary Role of Algorithm and Theory for Financial Statement Fraud” investigates the nature of relationship of theory building research and machine learning algorithms. Recent research on financial statement fraud has seen an increase in the use of machine learning techniques. While some machine learning based studies build on variables from causal inference research, others claim that machine learning algorithms work better with raw accounting variables and thus cast doubt on the role of causal inference research in the realm of machine learning. This study shows that ratio-based models, incorporating a synthesis of ratios from extant research and those computed from raw variables, consistently outperforms models based on raw variables. Also, ratio-based models outperform models based on combining ratios and raw variables. The results provide evidence supporting the complementary role of machine learning algorithms and theory building research in the realm of financial statement fraud.
The second study entitled, “Undetected Accounting Fraud: Implications for Theory and Machine Learning Predictive Models” examines the issue of undetected accounting fraud, its implication for theory development and predictive model building. Financial statement fraud, often referred to as accounting fraud, is a rare phenomenon, which makes it challenging to conduct research. Exacerbating the situation is the possibility that large number of accounting fraud went undetected due to resource constraint and shifting priority of the enforcement agencies. The extant literature on financial statement fraud, whether causal inference or predictive model, largely ignored the undetected fraud issue. The implication is that data used for research are not clean in that undetected fraud have been treated as “non fraud”, resulting in potentially biased estimates, invalid statistical inference, and sub optimal predictive model performance. This study applies several machine learning methods to identify non fraud observations with high confidence. We show that “cleaning” the data via these methods enhances inference in that several variables identified in prior literature change from insignificant to significant compared to the baseline of ignoring the undetected fraud issue. Further, “cleaning” the data via these methods significantly improve the performance of predictive model
Girl Gamers, Online Community, and Social Identity Theory: The Role of Positive Distinction Strategies on Group Engagement on r/GirlGamers
No full textIdentity theorists have studied the complex relationship between gender and gaming for over two decades, but research on how women persist in gaming communities despite harassment remains underdeveloped. Grounded in Social Identity Theory, this dissertation examines the subreddit r/GirlGamers to evaluate the prevalence and impact of positive distinction strategies in countering identity threats. Based on a mixed-methods approach, the findings highlight three key strategies (group permeability, social creativity, and social competition) alongside themes of emotion, structural inequality, and interpersonal violence. Statistical tests indicate that posts employing at least one positive distinction strategy receive significantly more upvotes and comments than those that do not, even when controlling for other predictors. Results suggest that women use positive distinction strategies to mitigate identity threats, a practice encouraged within r/GirlGamers due to the ongoing culture of gendered hostility in gaming
Understanding the Prevalence of γ-hydroxybutyric Acid in Hair for Clinical and Forensic Applications
No full textSexual assault affects over 400,000 Americans annually, including 13% of college students, yet only 2.5% of assailants face incarceration. This disparity stems from significant challenges in evidence collection, particularly in drug-facilitated crimes (DFCs). Victims often delay reporting due to fear, self-blame, or unawareness of the assault which is common in cases involving substances like γ-hydroxybutyric acid (GHB). Current forensic standards rely on blood and urine, which offer detection windows of just 48 and 120 hours, respectively. These short timeframes frequently fail to capture critical toxicological evidence, leaving investigators with only victim testimony, which the judicial system struggles to act upon without corroborating physical proof. To address this gap, this thesis explores hair as an alternative matrix, capable of retaining drug evidence for a minimum of two months and providing a chronological toxicological profile.
Hair analysis, while promising, faces significant hurdles. Unlike blood or urine, it requires time-consuming preparation before extraction can begin. External factors such as cosmetic products, UV exposure, and contamination can alter drug concentrations, and no standardized protocols exist for analysis or interpretation. These challenges are amplified when targeting GHB. GHB’s endogenous presence in humans, short half-life (t1/2 = 30 minutes), and large inter-individual variation prevents the establishment of universal concentration cut-off. Instead, individuals must serve as their own baselines, though interpreting such data lacks consistency.
A comprehensive literature review revealed gaps in extraction techniques, decontamination protocols, and analytical workflows, highlighting the need for standardized forensic approaches to differentiate endogenous from exogenous GHB. This research developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method to detect GHB and other DFC drugs in hair, achieving effective separation using C18 and hydrophilic interaction liquid chromatography (HILIC) columns in both positive and negative ionization modes. Three extraction methods, NaOH digestion, methanol solvent swelling, and M3 reagent, were evaluated for GHB recovery. The M3 reagent proved most reliable, yielding approximately 30% recovery across 11 trials, though matrix-dependent variations emerged when comparing synthetic hair, authentic human hair, and hydrolyzed keratin.
Despite these advancements, full method validation was not achieved within the study’s timeframe due to GHB’s low recovery, signal suppression, and matrix effects. Nevertheless, this work provides practical contributions: optimized mass spectrometry transitions, two chromatographic separation methods, and methodological guidance for forensic scientists. Experimental and literature evidence confirmed GHB’s complexity as a target analyte, while the study demonstrated awareness and identified key challenges for future resolution
Development and Application of Computational Tools for Data-Driven Materials Science.
No full textModern materials science generates vast amounts of data from computational simulations and experiments, creating significant challenges for data processing and analysis. This thesis addresses these challenges through the development and application of computational tools within the framework of Material Data Science (MDS). Contributions span the four pillars of MDS: Material/Molecular Data, Algorithms, Databases, and High-Throughput Processes—with a primary focus on the Algorithm, Data, Database pillars.
For the Algorithm pillar, two Python libraries were developed to streamline common analysis tasks. PyProcar simplifies the post-processing and visualization of electronic structure data (band structures, density of states, Fermi surfaces) obtained from various Density Functional Theory (DFT) codes, providing a unified interface for researchers. MechElastic automates the calculation of mechanical properties (stability criteria, elastic moduli, anisotropy) from DFT-computed elastic tensors for both bulk and 2D materials, reducing manual effort and standardizing analysis.
Computational studies on the binary NiTi and TiAu systems served both as applications of these tools and contributions to the Data pillar, generating datasets on low-energy structures and their properties. These studies highlighted the need for efficient data management, motivating contributions to the Database pillar. ParquetDB was created as a lightweight database framework utilizing the columnar Parquet format for efficient storage and retrieval of large, complex datasets within Python workflows. MatGraphDB extends this foundation, providing specialized tools for managing graph-structured materials data, crucial for representing atomic connectivity and knowledge relationships.Collectively, the tools and methodologies developed in this thesis enhance the ability to manage, process, analyze, and interpret complex materials data. By addressing key bottlenecks in the computational materials science workflow, this work supports the advancement of Material Data Science and contributes to accelerating the discovery and design of new materials
