Electronic Thesis or Dissertation

The Fountas and Pinnell Leveled Literacy Intervention System (LLI), first edition, is a textbook series designed for struggling elementary school readers. The materials have become entrenched in the nation’s schools and are currently utilized as an intervention resource in all fifty states and four of the seven largest school districts in Florida. Reading intervention support is a requirement for students in most states, often due to their performance on standardized assessments (Diffey, 2016). Moreover, NAEP data indicates that students of color are overrepresented in reading intervention courses; thus, instructional materials choices made for these courses disproportionally affect this population (The Nation’s Report Card, n.d.). As culturally relevant texts are academically beneficial, it is critical that intervention materials are appropriately representative (Aronson & Laughter, 2016; Au, 2001; Sampson & Garrison-Wade, 2011). The purpose of this qualitative critical content analysis of the 731 books within the LLI system was to examine the cultural, ethnic, and racial representation of people/characters of color within the series.
Major findings revealed that people/characters of color were depicted from a deficit model (Ladson-Billings, 2018). Coded information revealed 41.5% included a negative characterization while 7.6% offered a positive portrayal. Further, the books exploring the experiences and cultures of people/characters of color depicted undesirable conditions 25.7% of the time while presenting favorable information 2.5% of the time. The final finding centers on what is missing from the stories. Other than a select few texts, the LLI books are colorblind, presenting students an inaccurate view of society. Accordingly, counternarratives and stories that center on social justice/equity are notably absent.

Acute pH sensitivity of many neural mechanisms highlights the vulnerability of neurotransmission to the pH of the extracellular milieu. The dogma is that the synaptic cleft will acidify upon neurotransmission because the synaptic vesicles corelease neurotransmitters and protons to the cleft, and the direct data from sensory ribbon-type synapses support the acidification of the cleft. However, ribbon synapses have a much higher release probability than conventional synapses, and it’s not established whether conventional synapses acidify as well. To test the acidification of the cleft in the conventional synapse, we used genetically encoded fluorescent pH reporters targeted to the synaptic cleft of Drosophila larvae. We observed alkalinization rather than acidification during activity, and this alkalinization was dependent on the exchange of protons for calcium at the postsynaptic membrane.
A reaction-diffusion computational model of the pH dynamics at the Drosophila larval neuromuscular junction was developed to leverage the experimental data. The model incorporates the release of glutamate, ATP, and protons from synaptic vesicles into the cleft, PMCA activity, bicarbonate, and phosphate buffering systems. By means of numerical simulations, we reveal a highly dynamic pH landscape within the synaptic cleft, harboring deep but exceedingly rapid acid transients that give way to a prolonged period of alkalinization.

The goal of this study is to explore a novel concept of justice using allocations of outcomes and understanding the connection between those allocations and social cognitive variables. Justice is conceptualized through the lens of two opposing frames: consistency and compensatory. Participants assigned positive or negative outcomes to one of two hypothetical people, one person being depicted as “lucky”, the other as “unlucky.” A consistency sense of justice views justice as keeping the order of the world (positive with lucky), whereas a compensatory sense of justice understands it as a balancing act (positive with unlucky). ANOVA’s were ran and a single significant difference was found. In one condition, those whose had a consistency sense of justice had a significantly more internal locus of control than those who had a compensatory sense of justice. Further research will be needed to clarify why this difference did not emerge for all allocations.

Intensity modulated proton beam scanning therapy allows for highly conformal dose distribution and better sparing of organ-at-risk compared to conventional photon radiotherapy, thanks to the characteristic dose deposition at depth, the Bragg Peak (BP), of protons as a function of depth and energy. However, proton range uncertainties lead to extended clinical margins, at the expense of treatment quality. Prompt Gamma (PG) rays emitted during non- elastic interactions of proton with the matter have been proposed for in-vivo proton range tracking. Nevertheless, poor PG statistics downgrade the potential of the clinical implementation of the proposed techniques. We study the insertion of the nonradioactive elements 19F, 17O, 127I in a tumor area to enhance the PG production of 4.44 MeV (P1) and 6.15 MeV (P2) PG rays emitted during proton irradiation, both correlated with the distal fall-off of the BP. We developed a novel Monte Carlo (MC) model using the TOPAS MC package. With this model, we simulated incident proton beams with energies of 75 MeV, 100 MeV and 200 MeV in co-centric cylindrical phantoms. The outer cylinder (scorer) was filled with water and the inner cylinder (simulating a tumor region inside water-equivalent body) was filled with water containing 0.1%–20% weight fractions of each of the tested elements.

The term "collapse" has become a widely used term that oversimplifies the intricate histories of human-environment interactions. It has contributed to the belief that civilizations in the Americas and the tropics could not endure over time. However, the Manteño civilization of the Ecuadorian coast challenges this notion. Flourishing for a thousand years (ca. 650–1700 CE), the Manteños inhabited the neotropics at the gates of one of the world's most influential climatic forces, the El Niño-Southern Oscillation (ENSO). To thrive, the Manteños needed to navigate the extremes of ENSO during the Medieval Climate Anomaly (MCA, ca. 950–1250 CE) and the Little Ice Age (LIA, ca. 1400–1700 CE) while capitalizing on ENSO's milder phases. This research uses change detection analysis of Normalized Difference Vegetation Index (NDVI) on Landsat satellite imagery under various ENSO conditions from 1986 to 2020 in southern Manabí, where the 16th-century Manteño territory of Salangome was situated. The findings indicate that the cloud forests found in the highest elevations of the Chongón-Colonche Mountains provide the most resilient environment in the region to adapt to a changing climate. Further investigations of the cloud forest of the Bola de Oro Mountain using Uncrewed Aerial Vehicles (UAV) equipped with LiDAR, ground-truthing, and excavation uncovered a landscape shaped by the Manteños.

Online advertising [100], as a multi-billion dollar business, provides a common marketing experience when people access online services using electronic devices, such as desktop computers, tablets, smartphones, and so on. Using the Internet as a means of advertising, different stakeholders take actions in the background to provide and deliver advertisements to users through numerous platforms, such as search engines, news sites, and social networks, where dedicated spots of areas are used to display advertisements (ads) along with search results, posts, or page content.
Online advertising is mainly based on dynamically selecting ads through a real-time bidding (or auction) mechanism. Predicting user responses like clicking ads in e-commerce platforms and internet-based advertising systems, as the first measurable user response, is an essential step for many digital advertising and recommendation systems to capture the user’s propensity to follow up actions, such as purchasing a product or subscribing to a service. To maximize revenue and user satisfaction, online advertising platforms must predict the expected user behavior of each displayed advertisement and maximize the user’s expectations of clicking [28]. Based on this observed feedback, these systems are tailored to user preferences to decide the order in that ads or any promoted content should be served to them. This objective provides an incentive to develop new research by using ideas derived from different domains like machine learning and data mining combined with models for information retrieval and mathematical optimization. They introduce different machine learning and data mining methods that employ deep learning-based predictive models to learn the representation of input features with the aim of user response prediction. Feature representation learning is known as a fundamental task on how to input information is going to be represented in machine learning models. A good feature representation learning method that seeks to learn low-dimensional embedding vectors is a key factor for the success of many downstream analytics tasks, such as click-through prediction and conversion prediction in recommendation systems and online advertising platforms.

Boiling heat transfer associated with bubble growth is perhaps one of the most efficient cooling methodologies due to its sizeable latent heat during phase change. Despite significant advancement, numerous questions remain regarding the fundamentals of bubble growth mechanisms, a primary source of enhanced heat dissipation. This thesis provides a comprehensive examination of the mechanisms involved in the growth of bubbles during nucleate boiling. By conducting a combination of experiments and numerical analyses, the goal is to enhance our understanding of bubble growth phenomena and their impact on heat transfer. Initially, the experimental work focuses on comparing the heat transfer performance and parameters related to bubble dynamics between regular and modified fin structures. The findings demonstrate that the modified fin structure, which featured artificial nucleation sites, exhibits superior heat transfer characteristics. This improvement is attributed to changes in the bubble departure diameter, bubble departure frequency, and growth time. Subsequently, an artificial neural network is developed to accurately predict the bubble departure diameter based on the wall superheat and subcooling level. This predictive model provides valuable insights into bubble behavior originating from artificial nucleation sites.

Skin cancer is a prevalent cancer that significantly contributes to global mortality rates. Early detection is crucial for a high survival rate. Dermatologists primarily rely on visual inspection to diagnose skin cancers, but this method is inaccurate. Deep learning algorithms can enhance the diagnostic accuracy of skin cancers. However, these algorithms require substantial labeled data for effective training. Acquiring annotated data for skin cancer classification is time-consuming, expensive, and necessitates expert annotation. Moreover, skin cancer datasets often suffer from imbalanced data distribution.
Generative Adversarial Networks (GANs) can be used to overcome the challenges of data scarcity and lack of labels by automatically generating skin cancer images. However, training and testing data from different distributions can introduce domain shift and bias, impacting the model’s performance. This dissertation addresses this issue by developing deep learning-based domain adaptation models.
Additionally, this research emphasizes deploying deep learning models on hardware to enable real-time skin cancer detection, facilitating accurate diagnoses by dermatologists. Deploying conventional deep learning algorithms on hardware is not preferred due to the problem of high resource consumption. Therefore, this dissertation presents spiking neural network-based (SNN) models designed specifically for hardware implementation. SNNs are preferred for their power-efficient behavior and suitability for hardware deployment.

Assessment is frequently cited within the student affairs literature as a way of continuously improving programs, services, and events (Henning & Roberts, 2016; Upcraft & Schuh, 1996). However, the data collected through assessment is infrequently used to improve student affairs offerings due to practitioners’ fear, practitioner’s lack of training, a lack of leadership within the division or university, or an emphasis on assessment as a method of reporting results rather than improving offerings, such as programs, services, initiatives, or events (Cox et al., 2017; Fuller & Lane, 2017). In the limited published studies about how student affairs professionals use assessment data, many professionals admit they do not have a plan to use their assessment data and only a small number have a plan to use their assessment data to make changes (Beshara-Blauth, 2018; Cox et al., 2017; McCaul, 2015; Parnell et al., 2018; Ridgeway, 2014). The purpose of this qualitative study was to understand how student affairs directors who have been identified as exemplars use their assessment data to make changes. The research questions for the study were: 1) How do student affair directors use assessment data in their role to make changes? 2) How do student affairs directors learn to use their data to make changes? And, 3) What influences student affairs directors to use their data to make changes?

Complex life cycles are common across parasite taxa and frequently require trophic transfer of parasites from prey to predator; however, studies on parasite-host interactions often neglect variation in parasite life histories. Here I use two focal freshwater digenetic trematode species, Halipegus occidualis tongueworms and Haematoloechus complexus lungworms, as an empirical system to investigate how parasite life history traits drive host-parasite interactions across the life cycle. To examine how parasite life history and host ecology influence parasite genetic patterns, I characterized the genetic diversity of within-host infrapopulations, as well as overall population genetic structure, of sympatric tongueworm and lungworm populations. Infection load and genetic diversity of host-level parasite infrapopulations increased with host trophic level, highlighting the benefits of trophic transfer and multihost life cycles. Concurrently, first intermediate host population dynamics and dispersal ability played a role in predicting population-level parasite genetic diversity and structure.