Electronic Thesis or Dissertation

The Florida Everglades is a vast subtropical wetland that historically spanned over 1,000,000 hectares, but much of the Everglades has changed in the last 100 years due to anthropogenic activity. Collier County was subject to alteration in the form of logging, road building, and canal digging. These actions disrupted the natural sheet flow of water and had large environmental impacts on the region, impacts which are slowly being addressed by Everglades restoration efforts. The aim of this project was to observe the effects of environmental change at a cypress swamp forest in Collier County within the Big Cypress National Preserve. Using sediment core data including charcoal analysis, loss on ignition, and peat humification, as well as remote sensing techniques, this project uses a novel approach to assess local environmental conditions in the modern era. Historical records and contemporary data are used to evaluate change over time, and satellite imagery is used to quantify vegetative health. Modification of the environment related to anthropogenic activity is noted, and evidence of progress from restoration efforts is observed from the last two decades in our study’s data.

Previous research has demonstrated the harmful impacts of adverse childhood circumstances on adult romantic relationships. The current study examines the negative influence of childhood unpredictability on adult relationships and how this effect may be buffered and enhanced, respectively, by cognitive ability and Machiavellianism. A total of 256 participants completed measures of childhood unpredictability, Machiavellianism, cognitive ability, and relationship satisfaction. Individuals who experienced childhood unpredictability reported lower adult romantic relationship satisfaction. Machiavellianism was found to significantly enhance the negative effects of childhood unpredictability on adult relationships for men only. Cognitive ability was not a significant moderator.

The Miami Circle (8DA12) is a site in downtown Miami, Florida with a unique prehistoric feature. The feature is made up of small post holes which reflect a larger patterned arrangement that resembles a circle, most probably the remnants of a prehistoric structure. The Miami Circle was listed on the National Register of Historic Places in 2002 and declared a National Historic Landmark on January 16, 2009. The site has been linked to the Tequesta Indians.
This project utilized chi-square 𝑥2 test of independence statistical analytical methods using data obtained from the Miami Circle site and neighboring sites to determine the logical boundaries of the prehistoric village called Tequesta. Chi-square testing will determine if there is a statistically significant difference between the sites based on calculations of expected and observed frequency for sets of recovered artifacts.

The study explored whether middle school students’ select academic (grade point average [GPA], Accuplacer mathematics, reading, and writing scores, admission interview scores) and non-academic characteristics (race, ethnicity, the middle school they attended, their gender, their parents’ educational level) have any predictive power with regard to their success at an early college high school. This study compared binary logistic regression (BLR) and predictive discriminant analysis (PDA) statistical models. First-year early college academic success can be predicted using BLR and only six input factors from middle school (Accuplacer math scores, interview scores, gender, and race, as three dummy variables) with a 91% accuracy.
However, a student academic success prediction model using middle school data for a student’s first year college success could not be built. This finding asserts Astin’s Student Development Theory (1993, 1999a, 1999b), the theoretical framework that guided this study, that students can grow and improve over time and educators need to focus on cultivating and developing students’ smartness through high level instruction and coaching instead of identifying and celebrating smartness by accepting only the most prospective students into colleges (Astin, 1977, 1993, 1999a, 2017, 2018).

Learning to read is a fundamental skill that is understudied among children who hear more than one language at home. A particular limitation in the extant literature is that the samples are often Spanish-dominant and come from low-income households. This literature has evidence that Spanish skills – both language-specific (i.e., vocabulary) and language-general (i.e., phonological processing) – predict English reading ability. In the current study, we investigated the effect of oral language skills in both English and Spanish, as well as other pre-literacy skills, on the English reading skill of Spanish-English bilinguals who are English dominant and received English only instruction from school entry. The oral language skills and other pre-literacy skills of 101 Spanish-English dual language learners were assessed at 5 years. English reading skill was measured annually from 6 to 10 years. Latent growth curve analysis was used to model initial English reading skill at 6 years and the growth of English reading skill from 6 to 10 years. Four sets of hypothesized foundational skills measured at child age 5 years were tested as predictors of subsequent English reading skill: (1) English oral language skills, (2) Spanish oral language skills, (3) English and Spanish oral language skills, and (4) English and Spanish oral language skills, with other pre-literacy skills. The results of structural equation modeling indicated that English vocabulary and phonological awareness measured in Spanish were significant predictors when English and Spanish skills were entered separately. When English and Spanish oral language skills were included together, Spanish oral language skills did not explain English reading growth better than when oral language skills were modeled independently. The best model of predictors of English reading for bilingual 5-year-olds included only English vocabulary and English letter recognition.

Imagine Me Like That offers an exploration into an experience of one coming to terms with one’s unique trans and queer identity through ecological and nature-based connections, as well as through interpersonal connections. This collection utilizes both poetry and lyric essay to offer insights into the joys of queer ways of living, while also acknowledging the difficulties of occupying a marginalized identity. Ultimately, Imagine Me Like That seeks to affirm and acknowledge the multi-faceted modes of queer existence.

Using content analysis and in-depth interviews, this study finds variation in perspective of mental health in 1) how it is framed on social media platforms by mental health treatment advertisements and 2) how woman perceive their own mental health struggles, how they sought and maintain treatment, and how the culture of social media influences this perspective. To investigate this topic, this study is separated into two phases: Phase One is a content analysis of 25 mental health treatment advertisements for depression and/or anxiety on Facebook and Instagram with three questions in mind: 1. How do advertisements on social media frame depression and anxiety? 2. What are the solutions proposed? And 3. How are women represented in these advertisements? Phase Two consists of 14 in-depth interviews with three questions in mind: 1. How do women understand their mental health problems? 2. How do social media advertisements affect women seeking mental health treatment? And 3. How does social media affect current course of mental health treatment? Social media advertisements do medicalize women’s perspective of mental health and can best be understood in three terms: communication, convenience, and confidence, through an interplay of medicalization and gender framing. Women give meaning to their mental health through their experience in past and current life circumstances and the culture of social media has shifted understanding and engagement with this dynamic.

The ability to recognize human actions is essential for individuals to navigate through their daily life. Biological motion is the primary mechanism people use to recognize actions quickly and efficiently, but their precision can vary. The development of Artificial Neural Networks (ANNs) has the potential to enhance the efficiency and effectiveness of accomplishing common human tasks, including action recognition. However, the performance of ANNs in action recognition depends on the type of model used. This study aimed to improve the accuracy of ANNs in action classification by incorporating biological motion information into the input conditions. The study used the UCF Crime dataset, a dataset containing surveillance videos of normal and criminal activity, and extracted biological motion information with OpenPose, a pose estimation ANN. OpenPose adjusted to create four condition types using the biological motion information (image-only, image with biological motion, only biological motion, and coordinates only) and used either a 3-Dimensional Convolutional Neural Network (3D CNN) or a Gated Recurrent Unit (GRU) to classify the actions. Overall, the study found that including biological motion information in the input conditions led to higher accuracy regardless of the number of action categories in the dataset. Moreover, the GRU model using the 'coordinates only' condition had the best accuracy out of all the action classification models. These findings suggest that incorporating biological motion into input conditions and using numerical format input data can benefit the development of accurate action classification models using ANNs.

Adults with autism spectrum disorder (ASD) often experience co-occurring physical and mental health challenges throughout their lives. At the same time, they often face many barriers to accessing proper care. Strategies to improve communication and understanding can help to mitigate some of these barriers. This study examined the effects of an instructional package, including a video module accompanied by guided notes, the Autism Healthcare Accommodations Tool (AHAT), and behavior skills training (BST) to teach adults with autism to communicate their unique needs and request accommodations from a medical provider. Participants completed a self-guided video module that guided them through the process of completing the notes and AHAT to identify appropriate accommodations and prepare for a doctor’s appointment. Once completed, participants learned to verbalize their needs and accommodations through BST. Results showed that the participants’ communication skills increased after the introduction of the instructional package, generalized to a novel doctor and maintained once the instructional package was removed. Implications and suggestions for future research are discussed.

Adaptive cruise control (ACC) system is the first widely offered automated functionality that regulates the longitudinal movement of the vehicle using onboard radar sensors, and they can maintain a safe following distance with the preceding vehicle. In most of the field experiments with ACC-equipped vehicles conducted with internal combustion engine vehicles, there is still a gap in research on how the automation systems such as ACC combined with electric powertrains will influence the traffic flow be examined.
This study refined and recalibrated an ACC car-following model for EVs and integrated it into AIMSUN to realistically simulate ACC-equipped vehicles and their impact on the fundamental diagram of traffic flow. Simulations were conducted for various ACC market penetrations, and fundamental diagrams were constructed for those market penetrations using detector measurements at various locations along the simulated segment. Overall, the capacity and the jam density increase as the EV with ACC market penetration rises. EVs with ACC can achieve higher capacities compared to ICEs with ACC.