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The Price Cap Impact on Russian Crude Oil Exports: A Quantitative Assessment
Full text linkThis is a thesis project submitted toward the degree of Master of Science, Computational Science.The price cap imposed on Russian seaborne crude oil exports in December 2022 represents a novel economic measure in a world where sanctions have become a key tool for shaping geopolitical behavior. This study investigates the impact of the price cap on Russian crude oil export volumes, revenue, and destinations, using publicly available trade data alongside data visualizations and statistical analyses. It examines both the short- and long-term effects of the price cap, with the January 2021-December 2022 period serving as a baseline. The findings reveal that while the price cap successfully stabilized global crude oil markets and reduced Russian export revenue in the short-term, its long-term effectiveness was hindered by Russia’s circumvention strategies, such as the use of a shadow fleet, alternative trade routes, and strengthened partnerships with China and India. These adaptations enabled Russia to sustain export volumes and even restore revenue to near pre-price cap levels, suggesting that sanctions can be circumvented when sanctioned countries develop effective evasion tactics. This study underscores the challenges of enforcing economic sanctions and calls for more robust enforcement mechanisms, including real-time monitoring and international cooperation. The findings offer valuable insights for refining future sanctions policies to enhance their long-term effectiveness
“They”-ifying the Gender Binary: Mason Students, They/Them Pronouns, and Nonbinary Gender Identities
Full text linkThis thesis examines the nonbinary identities of a handful of students at George Mason University using autoethnography, digital ethnography, and participant observation, grounded in the queering of folkloristics. Due to a lack of information in many institutions and a lack of media representation on nonbinary identities, some nonbinary people turn to the internet and utilize placemaking practices to construct and negotiate their nonbinary identities. Nonbinary identities are liminal insofar as they can be transitional in the movement of one gender identity to another, and they can also be liminal in the sense of existing outside of or between cultural norms in cases where nonbinary people do not arrive at a binary gender identity. Nonbinary identities are impacted by cultural context, bodily surveillance, stereotyping, and ideas of cultural borrowing
Multi-omic health profiling in the critically endangered black rhinoceros (Diceros bicornis)
No full textThis work is embargoed by the author and will not be publicly available until May 2026.Black rhinoceros (Diceros bicornis; “black rhinos”) experience extinction threats in-situ because of poaching pressures. Simultaneously, the ex-situ population, which serves as a genetic reservoir against impending extinction threats, experiences its own threats to survival related to several disease syndromes not typically observed among their wild counterparts. The etiologies of these disease syndromes remain under studied. Further, the physiological mechanisms that underpin these syndromes are not fully understood. In this dissertation, we employed the use of multiomic profiling: metabolomic, immunoproteomic, metagenomic, and dietary metataxonomic datasets to identify the physiological pathways that appear perturbed in animals with disease phenotypes. We hope these findings will improve the overall health and wellbeing of black rhinos in managed care.2026-05-1
ENGINES OF AGENCY: IMPACT EVENTS IN THE DEVELOPMENT OF TABLETOP ROLEPLAYING GAMES
Full text linkThe advent of crowdfunding platforms like Kickstarter for creative projects has enabled potential backers to discover and interact with creators to a great degree. This high level of interaction, paired with the self-organizing and widely distributed cottage industry of creatives providing products, is a valuable site for the investigation of agency in the making of creative works. This dissertation seeks to iterate on established work in agency, workplace studies, actor-network-theory, and agentive modeling to build a model which analyzes agency in this context to better understand what interactions between actants influence the moment of opportunity – or Kairos – and subsequent ‘impact events’ that set the course of a creative project over time. This dissertation takes data from a 14-month workplace study of a tabletop roleplaying game and models how different relationships and interactions among the game studio, its staff and its backers result in particular creative and logistical decisions being made. In constructing this model, this study reveals the complex arrangements that influence creative decisions across the domains of work, play, time, geography, and culture. In particular, tensions are revealed between the concept of an individual as principal creator and a member of an organization or culture, and the tactics that are used to negotiate these tensions. This study also reveals a space between work and play largely facilitated by the advent of crowdfunding and inexpensive or free productivity software. In this space, a non-employer firm of one can operate as a group, a company built along a profit model is considered successful by simply breaking even, and the consumers of a product are intimately involved in its development. These findings suggest that the work/play and official/unofficial dichotomy is not sufficient in many cases, and that creative works built in a highly distributed fashion are consequently impacted by highly distributed interactions
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 3718.01
Full text link“The purpose of this paper is to present light curve follow-up validation observations of the TESS object of interest (TOI) 3718.01. This research aims to characterize and confirm the existence of TOI 3718.01 as an exoplanet or determine its status as inconclusive. Candidates for this experiment were identified using the Transiting Exoplanet Survey Satellite (TESS). Datasets were generated by George Mason University at the Herschel Observatory on December 6, 2023. AstroImageJ, a software package, was utilized to process, reduce, and plate-solve the data. Light curves were then obtained to confirm whether the TESS Satellite detected a transit. This work would confirm the planetary nature of TOI 3718.01, distinguishing it from other astrophysical phenomena that might mimic a planetary transit. This study aims to provide a comprehensive analysis of TOI 3718.01, contributing valuable insights into its potential status as an exoplanet, and enhancing our understanding of planetary systems identified by TESS.
Ground-based Light Curve Follow-up Validation observations of TESS object of interest TOI 5886.01
Full text link"By confirming the existence of exoplanets, we can gain valuable information to further explore the several planetary systems in our galaxy. The Transiting Exoplanet Survey Satellite (TESS) has identified several exoplanet populations, offering a large and diverse dataset for research. There are several methods for validating exoplanets, including the transit method, spectroscopy method, and direct imaging, each having its own advantages and limitations. We validated the transitional planet of the TESS, TOI-5886.01 in this paper through light-curve extraction and analysis using the transit and spectroscopy methods. This research aimed to validate whether the TESS candidate TOI-5886.01 is truly an exoplanet. The light curve analysis was conducted using the astronomy-based image processing software AstroImageJ. By examining variations in the target star's brightness relative to comparison stars, we aimed to identify transit signals that might be indicative of an orbiting exoplanet. If we are able to detect a drop in light intensity in the light curve, we can infer that there may be an exoplanet present. Our findings support the classification of TOI-5886.01 as an exoplanet, which provides characteristics of its light curve and other key parameters. However, additional observations and analyses are necessary to solidify its status as a confirmed exoplanet and rule out the possibility of a false positive.
Data-Driven Methods for Biological Network Dynamics and Feature Identification
No full textBiological networks are complex and discovering dynamics and underlying features for these large systems can prove difficult if conditions are not favorable. In this dissertation, we begin by reviewing current approaches in data science for dynamical system identification. We pay special attention to methods for inferring dynamical systems which do not require prior knowledge of the symbolic representation of the dynamics, such as Sparse Identification of Nonlinear Dynamics. With these methods in mind, we propose an alternative approach using the Non-negative Least Squares (NNLS) algorithm to infer the dynamics of a (biological) network from data. We will discuss how this approach can be used to identify dynamics for both mass-action systems as well as dynamical systems containing rational functions. On a similar note, we propose a data-driven method for the identification of system conservation law(s) in the absence of the knowledge of system dynamics. Conservation laws are an inherent feature in many systems modeling real world phenomena, in particular, those modeling biological and chemical systems. If the form of the underlying dynamical system is known, linear algebra and algebraic geometry methods can be used to identify the conservation laws. We develop a robust data-driven computational framework that automates the process of identifying the number and type of the conservation law(s) while keeping the amount of required data to a minimum. We demonstrate that due to relative stability of singular vectors to noise we are able to reconstruct correct conservation laws without the need for excessive parameter tuning. While we focus primarily on biological examples, the framework proposed herein is suitable for a variety of data science applications and can be coupled with other machine learning approaches. Finally, we extend conditions for adaptation for biological networks to include singular systems with non-hyperbolic equilibria and conditions in which this alternative criteria are needed. The proposed theoretical extension is compatible with the notions of homeostasis and robust perfect adaptation (RPA) and clarifies the relationship between the two. The new condition is derived using the notion of Moore-Penrose pseudoinverse and is implemented using a numerically efficient algorithm. The proposed approach is tested on several synthetic systems that are shown to exhibit homeostatic behavior yet lie outside of the scope of earlier work
A Web-based-Application Intervention to Improve Problem-Solving Skills Using A Self-Regulated Learning Microanalytic Protocol
Full text linkMathematical word problem solving has been a focus of study through a variety of literature bases. This research applies Self-Regulated Learning (SRL) microanalytic protocols as an intervention with students aged 11-22 with a focus on whether math achievement increases. Designing a SRL microanalytic protocol, requires a sequence of steps to define the central learning prompts, the SRL prompts at each phase, and strategic feedback. To deliver these materials at scale, a web-based platform was developed to host the content, record student answers through a variety of interfaces, and provide real-time strategic feedback by analyzing student scores for correctness and common misconceptions. This intervention delivered five similar protocols to 51 students in a pre- and post-test design.Throughout the intervention, high rates of attrition showed that students were not motivated by the intervention. Students omitted the SRL prompts at a higher rate than the mathematics word problems. Pre- and post-test data from the most consistent users of the intervention did not reflect significant growth. The trend in the SRL-specific data similarly did not reflect growth over time. Metacognitive monitoring data did reflect stronger skills by the college subsample than the sample aged 11-17 years old. Qualitative feedback through the final questions in each protocol reflected some technical challenges, but few overarching issues using the materials. Students replicated answers to the goal-setting prompt most, which indicates that this prompt could be asked with less frequency. Overall, the web-based interface was able to capture students’ math achievement and SRL skills, but did not serve as an intervention by itself. The conclusion investigates other ways to strengthen the intervention
CHANGES IN PRECIPITATION PATTERNS AND TRENDS USING DATASETS FROM MULTIPLE SOURCES AND ACROSS DIFFERENT SPATIAL SCALES
No full textThe need for reliable information on changing precipitation patterns stems fromaddressing current and future challenges in the hydrological, environmental, agricultural and water resources management sectors. Increasing anthropogenic emissions contribute to climate system disruption, which is ultimately accountable for changes in precipitation patterns. Therefore, after noticing the improvement in air quality during COVID-19 lockdowns and considering it as an anthropogenic aerosol reduced environment, changes in precipitation patterns are investigated in the Hubei Province, China and in Northern Italy using a global satellite-based precipitation dataset, IMERG (Integrated Multisatellite Retrievals for Global Precipitation Mission). Results indicate that overall rainfall averages were higher in the Spring of 2020 with respect to their corresponding climatological means, with higher standard deviations, especially in the more urbanized regions like Wuhan, China and Milan, Italy. Precipitation rates observed during thexv Spring of 2020 tend to fall outside of the climatological 25th – 75th percentile bounds. Similarly, the number of rainy pixels was higher in Spring 2020 than the climatological 75th percentile and sometimes even higher than the 95th one. These anomalies may be due to natural variations and may not be caused directly by the reduction in human induced atmospheric pollutant concentrations. Nevertheless, this analysis proves that precipitation patterns during the lockdowns were on the extreme tails of the precipitation climatological distributions for both regions of interest. As anthropogenic climate signals have intensified, precipitation patterns have changed over the Contiguous United States (CONUS) and may continue to change in the future. Comparing historical climate model simulations to ground-based observations can help us quantify uncertainties in climate models when simulating precipitation and its changes. This work evaluates precipitation simulated by the Community Earth System Model Version 2 large ensemble (CESM2- LE) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) against groundbased observations from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center Unified CONUS (CPC) during 1948-2022. As a second step, past precipitation patterns from CPC observations are compared to future projections (2023-2100) of CESM2 for a medium-to-high emission scenario (Shared Socioeconomic Pathways, SSP3-7.0) from a 70-member ensemble. Results indicate that precipitation variability is drastically reduced when averaging all 70 ensemble members and suggest caution when using the ensemble mean to draw conclusions regarding precipitation trends. CESM2 was found to underestimate (overestimate) ground observations over CONUS in summer (winter). Climate model simulations struggle particularly to capturexvi the high-magnitude precipitation (>10 mm/day in annual averages), especially in the Northwestern US. In order to avoid precipitation biases in the model, a pixel-by-pixel bias correction is conducted and systemic error between the model and observations is removed from the model future projections on moving forward with the analysis. Historical data show slightly upward patterns in annual, spring, fall, and winter averages, patterns that are projected to continue in the future. Future annual precipitation will increase with respect to historical observations by as much as 11% and 15% in already wet Northeast and Southeast regions respectively, whereas arid NGP region will experience up to ~15% decrease. Overall results indicate drier summers and wetter winters in the future with respect to the past. 75th and 95th percentiles of seasonal precipitation will become more extreme during winter by as much as ~100% and less extreme during summer in the future by as much as ~80%. This study places a strong emphasis on understanding reliable future climate projections, which can be useful when designing community-driven adaptation and mitigation plans for climate change. However, the spatial resolution of climate models may be too coarse to obtain reliable regional and local precipitation projections. High spatial resolution is important for assessing precipitation variability, especially in areas that are characterized by complex topographic features. Thus, our ability to identifying precipitation patterns and how they change over time can be impacted by the spatial scale of the data used to detect such trends. This work uses the CPC dataset during 1948-2015 across the Contiguous United States (CONUS). Starting from its native resolution, i.e., 28 km, the CPC product is upscaled to 50 km, 100 km (i.e., the scale of many global climate models), and to axvii regional scale (based on the National Climate Assessment (NCA) regions). For each of the four different resolutions, Theil-Sen trends of precipitation volume (total annual precipitation), frequency (number of wet days in a year), and intensity (daily maximum precipitation) are analyzed. Results indicate that some of the finer details of information on trend magnitudes and their statistical significance can be overlooked when aggregating to coarser scale, especially at regional scale. However, the non-normality and heterogeneity of the precipitation distribution makes it difficult to predict how trends and their levels of significance change when we transition from finer to coarser resolution. The distribution of trend values exhibits minimal variation between the scales of 28 km and 50 km, whereas a more pronounced change is observed when upscaling to 100 km. Generally, the maximum (minimum) trend values decrease (increase) from finer to coarser scales for both CONUS and the single NCA regions. Extreme trend values (both maximum and minimum) show large differences between resolutions in topographically complex regions. Overall findings from this study indicate that high resolution data are fundamental to detect precipitation patterns and their changes at the local-to- subregional scale and can guide future investigations on selecting the appropriate spatial scale when evaluating precipitation patterns. The research questions posed in this dissertation are the following: 1. Are precipitation patterns affected by anthropogenic aerosol emissions? 2. How does a large-ensemble climate model compare to ground observations when detecting changes in past precipitation?xviii 3. How have precipitation patterns changed in the past century and how will they change in the later decades of the 21st century across CONUS? 4. What information might be overlooked at coarser resolutions and what is the appropriate spatial scale when evaluating precipitation changes in different climatic regions
A Mixed-Methods Investigation of A Self-Regulated Learning Intervention on Student Veteran and Non-Traditional Student College Success
Full text linkAs student veterans and service members (SVSM) continue to pursue post-secondary education, the body of literature contains multiple studies of transition and acculturation, but little empirical assessment of programs and services to support SVSM and address factors of student success, including academic achievement, engagement in educationally purposeful activities, satisfaction, acquisition of desired knowledge, skills and competencies, persistence, and attainment of educational objectives. The current study fills that gap through examination of a self-regulated learning intervention in an introductory seminar course. I combine Bronfenbrenner’s ecological systems and social cognitive theory as a framework to help illuminate factors that influence the results of the study. Through a mixed-methods study of longitudinal concurrent explanatory design, a quasi-experimental intervention was combined with interview evidence to describe the differences and understand how changes occur in student success variables as a result of a self-regulated learning intervention. Participants were recruited from two sections of ALDC 300 undergraduate students, including SVSM and non-traditional students, in an adult learner degree completion program, ALDC Program, at a large, public, research one (R1) institution in a mid-Atlantic state, Metro U, to participate in the study. As a result of the intervention, the expected changes in self-regulated learning behaviors and elements of student success were not seen; only limited increases in success variables were seen. Interviews revealed what participants did and did not learn as a result of the intervention, and themes described the complexity of life and robust support systems that impact student success. Themes of time and the value of a degree were also influential to student success. Mixing also shed light on multiple threats to validity which would not have been discovered in a monomethod study