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Media representation of the new Disney princesses: portrayal of gender roles
Get PDFThis study explores the evolving representation of gender roles in the Disney Princess movies: "Tangled" (2010), "Moana" (2016), and "Raya and the Last Dragon" (2021). Utilizing semiotic methodology, the research examines Ronald Barthes theory on how these films both challenge and perpetuate cultural myths and ideologies related to gender roles focused on a Western-Euro American perspective. The investigation focuses on the portrayal of New Disney Princesses and their alignment with contemporary mythological constructs. By analyzing the visual, textual, and symbolic elements in these movies, the study seeks to uncover the interplay between cultural themes and narrative structures, and how they reflect and influence societal narratives and cultural ideologies. The research contributes to scholarly discourse on media representation and gender dynamics, highlighting the evolving roles of Disney Princesses as influential cultural artifacts in shaping societal values, particularly among younger demographics. This study is significant in understanding the reciprocal influence between media and societal ethos, offering insights into the fabric of contemporary mythology as it intertwines with popular media
Machine learning for classifying biological radar echoes with S-band polarimetric radar
No full textThe S-band WSR-88D weather radar is sensitive enough to observe biological
scatterers like birds and insects. However, their non-spherical shapes and frequent
collocation in the radar resolution volume create challenges in identifying their
echoes. We propose a method of extracting bird (or insect) features by coherently
averaging dual polarization measurements from multiple radar scans, containing
bird (insect) migration. Additional features are also computed to capture aspect
and range dependence, and the variation of these echoes over local regions.
Next, ridge classifier and decision tree machine learning algorithms are trained,
first only with the averaged dual pol inputs and then different combinations of the
remaining features are added. The performance of all models for both methods,
are analyzed using metrics computed from the test data. Further studies on differ-
ent patterns of birds/insects, including roosting birds, bird migration and insect
migration cases, are used to investigate the generality of our models. Overall, the
ridge classifier using only dual polarization variables was found to perform con-
sistently well across all these tests. Hereafter, this model is called the Bird-Insect
Ridge Classifier (BIRC).
Enhancements of the Velocity Azimuth Display (VAD) Wind Profile (VWP)
are explored by integrating BIRC to generate three new VAD products namely:
the insects-birds ratio, VAD focused on birds and insects focused VADs. Two
mains findings are drawn from experiments on these products. First, the insects-birds ratio is found to have an inverse relationship to wind biases, confirming bird
contamination as a cause of the latter. Second, VAD wind biases can be reduced
by focusing VAD on insects instead of all biological echoes.
It is recommended that BIRC can be used on the WSR-88D, for classifying
biological echoes from the HCA as birds or insects. Furthermore, the insects-birds
ratio, bird VAD and insect VAD products can be incorporated into the VWP. To
the best of our knowledge, this is the first machine learning classifier that has
been demonstrated to simultaneously classify diverse patterns of bird and insect
echoes, and improve clear-air VAD wind estimation by incorporating taxonomic
information
A 20-Year Survey of Short-Duration Extreme Rainfall Events from Summertime Convection Over the Central and Eastern United States: Climatology, Trends, and Meteorological Patterns
No full textShort-duration extreme rainfall events (EREs) caused by convection are often associated with flash flooding, which can have devastating impacts on society. An increase in the frequency and intensity of EREs has been documented over multiple continents with evidence of a direct link to anthropogenic climate change. Over the central and eastern Continental United States (CONUS), EREs peak in frequency during June, July, and August (JJA) due to the summertime maximum in convective activity, with the most significant EREs resulting from mesoscale convective systems (MCSs). These MCSs and their associated rainfall have a nocturnal maximum over the central CONUS.
This study utilizes gridded hourly Stage IV precipitation analyses to detect short-duration EREs and record their properties over the central and eastern CONUS over a 20-year period (2003–2022). The Stage IV dataset consists of gauge-corrected radar-derived quantitative precipitation estimates on a 4-km grid, which has the advantage of capturing localized extreme rainfall that can occur between rain gauge sites. Extreme rainfall is defined in this study when the 12-hour accumulation exceeded the 10-year average recurrence interval threshold at that location based on the NOAA Atlas 14 dataset. All nearby grid points simultaneously exceeding the threshold were grouped into event objects. Several spurious events that were detected due to errors in the Stage IV dataset were filtered out using consistent quality control procedures.
Results of the 20-year climatology mainly solidify previous studies, but this study provides additional quantitative evidence that nocturnal MCSs are the most prolific producers of extreme rainfall over the domain during JJA. Unfortunately, the accurate prediction of nocturnal convective rainfall has been shown to be a challenge in numerical weather prediction models. In addition, the highly localized and chaotic nature of the extreme rainfall is revealed, motivating the need for high resolutions in precipitation data and numerical models.
The few previous studies that utilized Stage IV analyses for the purposes of studying EREs did not examine interannual or long-term changes in ERE frequency or characteristics. With acknowledgments of the potential caveats of using the Stage IV dataset, this study discovered statistically significant increasing trends in ERE frequencies through the 20-year period that are dominated by MCSs during JJA, as opposed to more localized convection. Despite the short period of record, this finding aligns with previous studies suggesting an increase in heavy rainfall from MCSs in a warming climate.
This thesis also discusses the discovery of a wide range of interannual variability in the frequency and severity of the JJA convective EREs. Composite and correlation analyses using reanalysis fields reveal statistically significant large-scale meteorological patterns that may help explain this variability, which can potentially aid in medium and long-term forecasting. These patterns include higher low–mid-level moisture over the Southern and Central Great Plains, enhanced southerly moisture transport from the western Gulf Coast to the Midwest, and enhanced mid-level ridging over the southeastern CONUS. Bolstered by composite analysis of five intense nocturnal EREs over the central CONUS, there is a strong argument that the enhanced moisture transport is driven by a westward expansion of the climatological North Atlantic Subtropical High into the eastern CONUS. Higher geopotential heights over the eastern CONUS relative to the Rockies results in an enhanced pressure gradient and southerly flow over the central CONUS, leading to a stronger and/or more frequent low-level jet. The low-level jet is a key ingredient in the development of nocturnal extreme-rain-producing MCSs. However, the low-level jet observed in this study extended well to the east of the climatological low-level jet driven by the sloping terrain of the Great Plains. With studies suggesting a westward expansion of the North Atlantic Subtropical High in a future climate, the increase in intense MCS-related EREs over the central CONUS during the summer is likely to continue
Jewish Community Views on Partnerships with the Judaic Studies Department
Get PDFThe purpose of this study was to better understand the dynamics of partnerships between Judaic studies departments and the Jewish community and to what degree Jewish community members find these partnerships useful through community-focused perspectives. This study utilized a qualitative approach with a narrative research design. A narrative design allowed Jewish community members to tell in-depth stories of their experiences in partnering with their local Judaic studies departments. Critical Social Theory (CST) combined with Community-Based research (CBR) formed the foundation of this study’s theoretical framework. Results showed that although Jewish community members noted many perceived benefits from partnering with their local Judaic studies department, they also emphasized that there were many instances where they saw room for improvement
Integration and School Choice: Challenges and Opportunities for School Leaders
Get PDFTransformations brought about by emerging models of school choice could shape the future of school integration. As such, developing an understanding of emerging models of school choice is important to the Equity Assistance Center Program’s mission of school integration and equal educational opportunities for all students.The contents of this document were developed under a grant from the U.S. Department of Education (S004D220003).YesPeer reviewed by the MAP Center. Peer review was not blind
Addressing mobility challenges in AI-enabled emerging cellular networks
No full textThe ability to support seamless user mobility has been the Raison D’etre of mobile networks. In recent years, the cellular network industry has evolved significantly, and mobility management is becoming a major challenge. This is due to factors such as densified deployment, stringent Quality of Service (QoS) requirements of diverse use cases like next-generation URLLC, and evolved network architecture. In this thesis, we identify and tackle the pressing challenges of mobility management in emerging cellular networks, which if not addressed, can potentially become the network's Achilles' heel. We address three primary challenges: lack of suitable tools for investigating mobility, radio link failures due to handover failures, and limitations of the existing handover parameters in the standards for mobility management optimization.
To address first challenge, we propose a tri-pronged approach: developing a computationally efficient and realistic mobility simulator, designing and deploying a state-of-the-art experimental testbed called TurboRAN, and introducing AZTEK, an AI-enabled handover parameter optimization framework that can work with limited training data generated either from the simulator, testbed or real network.
To address the second challenge, we propose and evaluate TORIS (Transmit Power Tuning-based Handover Success Rate Improvement Scheme), a novel data-driven solution to reduce inter-frequency handover failures. TORIS consists of an AI-based model to predict handover failures and a heuristic scheme for tuning the transmit power of cells. Unlike conventional methods, TORIS proactively adjusts cell power when a handover failure is anticipated.
To address the third challenge, we propose and analyze a novel parameter called user individual offset (UIO), which considers user-specific behaviors (i.e., speed, direction, service requirement) for value selection. Our results show that UIO can help resolve long-standing challenges in mobility management without tradeoffs between key performance indicators
A Study of Teacher Burnout During the COVID-19 Pandemic
Get PDFTeaching has become an increasingly challenging profession with growing class sizes, dwindling resources, expanded administrative responsibilities, and a perceived lack of support. These factors have contributed to rising rates of emotional exhaustion. The addition of teaching during the COVID-19 pandemic only intensified the situation. Prior work on teacher burnout focuses on qualities such as: poor working conditions, time, family conflicts, hours worked, and school type (Milfont et al., 2008). Principal support of teacher psychological needs (PSTPN) is a relatively new construct about supporting a teacher’s autonomy, competence, and relatedness. The literature shows that through all types of school leadership (instructional, transformational, and collective) the foundation of leadership is about conversation and relationships. Using self-determination theory as a theoretical lens, this quantitative study analyzes the University of Oklahoma’s annual climate survey that is distributed to teachers within two metropolitan school districts. The study captured the level of teacher burnout prior to and during the COVID-19 pandemic, if teachers experienced principal support for their psychological needs prior to and during the pandemic, and if there is a relationship between PSTPN and teacher reported burnout prior to and during the pandemic. Analyses include descriptive statistics and a series of regression models. This study shows that principal support of teacher’s psychological needs through informal and formal conversations may lead to decreased teacher burnout
Spatial and temporal perspectives on cyanobacterial ecological dynamics using space-based and genomic approaches
No full textToxin-producing cyanobacteria, a type of blue-green algae, rank high among the global problems facing aquatic systems. Under certain conditions, particularly elevated nutrient concentrations created by agricultural and urban runoff, as well as altered temperature regimes driven by changing climate, cyanobacteria can form ecosystem disrupting blooms. Their toxins produce chemicals that are harmful, not only to other aquatic organisms, but to humans, pets, and terrestrial wildlife that come into contact with cyanobacterial toxins in water bodies. As such, harmful cyanobacteria blooms threaten freshwaters used for recreation, fisheries, and drinking water. Given the intricate dependency of humanity on water, and the concentration of population centers surrounding lakes and reservoirs, it is imperative that we find new and innovative ways to detect, monitor, manage, and mitigate cyanobacteria blooms. My research addresses these needs in multiple, but complimentary ways, and considers scales that range from satellite data that provides a vantage of blooms from hundreds of kilometers away, to next generation sequencing data that provides a view of how bloom communities form at the genomic level.
In my first two chapters, I search for the existence of a cyanobacteria-bacteria interactome. Cyanobacteria have evolved very small genomes compared to eukaryotes, and thus trade a reduced set of metabolic functions for energy savings during replication. It has been hypothesized that cyanobacteria rely on other bacteria in the community to perform these “lost” metabolic functions. This interaction between the cyanobacterial-bacterial community is termed an interactome and understanding this relationship could help us create innovative biological mitigation strategies in the future.
In chapter one, as an initial test of the cyanobacteria-bacteria interactome hypothesis, I examined a globally occurring, colonial cyanobacterium, Microcystis aeruginosa, and its associated bacteria, testing the hypothesis that if Microcystis depends on bacteria for certain metabolic functions, then geographically distinct Microcystis blooms should harbor similar bacterial microbiome communities and metabolic functions. While I found that the bacteria species were different across nine lakes, the metabolic functions contributed by the associated bacteria were similar across globally distributed lakes, thus supporting the cyanobacteria-interactome hypothesis.
In chapter two, I focused on the hypothesized interactome of a different bloom forming cyanobacteria, Raphidiopsis. Unlike Microcystis, which has a mucilaginous outer layer that harbors a community of bacteria in close physical proximity to its cells, Raphidiopsis is a filamentous cyanobacteria, forming distinct multicellular trichomes with no sheath. I hypothesized if Raphidiopsis selects for certain bacteria, this would result in greater similarity in bacterial microbiome community composition within bloom phases than across bloom phases in the same year. After examining three years of bloom data, I found that bacterial communities associated with Raphidiopsis blooms were more similar to other communities in the same phase than communities within the same year. These results point toward Raphidiopsis selection of associated bacteria and are a first step in providing evidence for the existence of a co-evolved interactome.
In my third chapter, I move from genomes to satellites to explore rapid and accurate ways of detecting and monitoring cyanobacteria blooms. Rigorous monitoring for harmful cyanobacteria blooms is necessary to manage risks to animal and human health, but is often expensive and time-consuming. Remote sensing using satellite- and ground-based platforms has the potential to allow frequent and cost-effective monitoring of cyanobacterial blooms on lakes. Here, I test the capacity of satellite- and ground-based sensors to accurately predict cyanobacteria blooms in Oklahoma. Contrary to research on larger, natural lakes, I found Landsat satellite models performed poorly and were unable to accurately detect blooms in Oklahoma reservoirs. The ground-based models, on the other hand, were highly accurate at detecting blooms. My results indicate alternatives to satellites, like ground-based sensors, should be further explored as reliable and inexpensive tools for monitoring harmful cyanobacteria blooms on large numbers of small lakes and reservoirs