Electronic Thesis or Dissertation

This experiment used different methodologies and comparisons that helped to determine the direction of future research on water-based perception systems for unmanned surface vehicles (USV) platforms. This would be using a stereo-vison based system. Presented in this work is object color and shape classification in the real-time maritime environment. This was coupled with HSV color space that allowed for different thresholds to be identified and detected. The algorithm was then calibrated and executed to configure the depth, color and shape accuracies. The approach entails the characterization of a stereo-vision camera and mount that was designed with 8.5° horizontal viewing increments and mounted on the WAMV.
This characterization has depth, color and shape object detection and its classification. Different shapes and buoys were used to complete the testing with assorted colors and shapes. The main program used was OpenCV which entails Gaussian blurring, Morphological operators and Canny edge detection libraries with a ROS integration. The code focuses on the area size and the number of contours detected on the shape for successes. A summary of what this thesis entails is the installation and characterization of the stereovision system on the WAMV-USV by obtaining specific inputs to the high-level controller.

Estuarine ecosystems are dynamic habitats, where the convergence of marine and freshwater results in constant fluxes in environmental abiotic parameters. Organisms must balance these variations within their optimal range to minimize physiological costs, often by movement from unsuitable to more suitable areas. Additional disruptions to ecosystem balances, such as anthropogenic hydrologic discharges, further alter environmental conditions and may cause population-wide movement responses of mobile organisms. Responses to anthropogenic and natural fluctuations can differ based on time of year, life history stage, or individual characteristics. These ecologically-balanced dynamics are difficult to model. In this study, I examined variability in estuarine environmental data and common snook (Centropomus undecimalis) movement responses to anthropogenic and natural fluctuations in the environment in a managed waterway. ARIMA time series models were tested as a method of modeling variability in environmental parameters. Monthly variance was well described throughout most of the estuary, especially when the interannual and intra-annual patterns were stable, indicating that these models are a good method for these types of data and could be appropriate for forecasting. Euryhaline sportfish movement responses to high discharge events in a managed waterway were observed with passive acoustic telemetry and did not show large-scale, population-wide consistency. Responses were variable between and within individuals, but individual characteristics appear to have influenced behavior in response to disturbances. Thus, these sportfish populations may be more resilient to this type of disturbance than previously hypothesized. Generalized additive mixed effects models showed that the distribution and movement of individual fishes varied in response to multiple natural and anthropogenic factors, and there was no primary driver. The understanding of the relationships among the distribution and movement of fishes and abiotic and anthropogenic factors can guide management of waterways and provide insight into how changes will affect abiotic factors and communities.

Stony coral tissue loss disease (SCTLD) has spread throughout the entirety of Florida’s Coral Reef (FCR) and across the Caribbean, impacting over 50 coral species. The threatened hermatypic coral, Orbicella faveolata, demonstrates intraspecific variation in SCTLD resistance and has been categorized into three resistance levels: “High”, “Medium”, and “Low”. This study evaluated potential genomic underpinnings of variable disease susceptibility by monitoring and sampling 90 O. faveolata colonies from Southeast Florida and the Lower Keys. High resolution analyses of >11,000 single nucleotide polymorphisms identified no significant genetic lineages among O. faveolata colonies associated with SCTLD resistance or susceptibility. Algal symbiont community analysis revealed that Durusdinium was present in greater abundances in SCTLD-affected colonies compared to the “High” resistance group, suggesting that algal symbiont community structure may play a role in SCTLD resistance. Data generated by this research will be combined with complementary approaches to further investigate the complex drivers of SCTLD resilience.

The purpose of this study was to develop and explore the factor structure of the Student Engagement in Social–Emotional Learning Skills (SE-SELS) survey, a newly designed assessment that assesses students’ knowledge and use of five social–emotional learning skills (self-awareness, self-management, social awareness, relationship skills and responsible decision making) aligned with the framework of the Collaborative for Academic, Social, and Emotional Learning. The SE-SELS survey includes 20 items and takes approximately 5 min to complete. Participants were 359 students in Grades 6–8 at one university-affiliated laboratory school serving students in kindergarten through eighth grade. The instrument was explored through exploratory factor analysis, which determined that the strengths of relationships among variables was satisfactory and that the model overall had strong internal reliability (α = .90). All items were retained for the final SE-SELS survey (.41 ≤ α ≤ .75). Items linked to the relationship skills component and the self-management component were merged to create a four-factor model, which ABSTRACT was a better fit for the data overall and retained all five components addressed by the SESELS survey. There is at present a lack of reliable and valid instruments that measure the effectiveness of interventions and student outcomes related to social–emotional learning. The SE-SELS survey can thus help school counselors and other educators to determine the impact of social–emotional learning interventions and provide baseline and growth data for students. Follow-up studies are needed to support the reliability and validity of the SE-SELS survey.

It is well known that in the near future, a large-scale quantum computer will be unveiled, one that could be used to break the cryptography that underlies our digital infrastructure. Quantum computers operate on quantum mechanics, enabling exponential speedups to certain computational problems, including hard problems at the cornerstone of our deployed cryptographic algorithms. With a vulnerability in this security foundation, our online identities, banking information, and precious data is now vulnerable. To address this, we must prepare for a transition to post-quantum cryptography, or cryptosystems that are protected from attacks by both classical and quantum computers. This is a dissertation proposal targeting cryptographic engineering that is necessary to deploy isogeny-based cryptosystems, one known family of problems that are thought to be difficult to break, even for quantum computers. Isogeny-based cryptography utilizes mappings between elliptic curves to achieve public-key encryption, digital signatures, and other cryptographic objectives necessary to support our digital infrastructure's security. This proposal focuses on three aspects of isogeny-based cryptography: 1) cryptographic engineering of isogeny-based cryptosystems; 2) developing and optimizing security-enabling isogeny applications; and 3) improving the security from known and emerging implementation attacks. By improving each of these aspects, we are providing confidence in the deployability of isogeny-based cryptography and helping to prepare for a post-quantum transition.

Studies suggest that smokers have less than optimal immune responses to natural infections and booster vaccines, which may adversely influence the herd effects of vaccines. We hypothesize that smoking attenuates preexisting memory cells and antibodies specific to childhood immunizations. To test this, we first evaluated several in vitro culture conditions that mimic in vivo immune cell responses within human blood samples. This study concluded that among tested conditions, R848/IL-2 and GMCSF/CD40L/IL-2 optimally supported the differentiation of existing antigen-specific memory B cells into immunoglobulin-secreting plasma cells. Additionally, GM-CSF optimally supported the differentiation of antigen-specific memory T cells into IFN-γ- producing effector cells. Overall, we have established culture conditions that will allow us for the first time to assess the impact of external factors (i.e., smoking, immunosuppressive drugs, etc.) on preexisting, development, and longevity of immune memory specific to childhood, booster, and new vaccines among various populations.

The aim of the study was to validate the researcher's new therapeutic intervention, Sign your Feelings, through a four-week randomized control trial (RCT). This intervention involves hearing therapists working with hearing clients. It incorporates (a) discussions about the therapeutic alliance, (b) therapists teaching their clients 36 signs in American Sign Language (ASL) and (c) frequent emotional disclosure as clients learn and practice the signs. This RCT measured the therapeutic alliance and client outcomes, using the Sessions Rating Scale (SRS) and CORE-10 instrument. Eighty adults from 10 U.S. States participated in the study. Sixteen therapists underwent training before administering the intervention, and sessions took place virtually and in-person.
Results did not render statistically significant differences for SRS total scores between the two groups (p =.194). CORE-10 total scores between the two groups were not statistically significant either (p = 0.736). However, results did show a statistically significant negative correlation of r = -.229 (p = .041) between SRS and CORE-10 post-test scores.

This study investigated the influence of high concentrations of porewater H2S (~100 μM) on recruitment of the tropical dominant seagrass species, Thalassia testudinum, following mortality events or "die-offs" in Florida Bay. Major seagrass die-off events (>50 km2) are occurring globally in coastal regions with mortality frequently linked to hypoxia and sediment-derived hydrogen sulfide (H2S) exposure, a well-known phytotoxin. In tropical carbonate environments, such as Florida Bay, low iron in sediments promote H2S accumulation and subsequent intrusion into seagrass meristematic tissue through roots, and root-shoot junctions. While H2S intrusion into meristematic tissue is a leading hypothesis for large-scale seagrass mortality events, it is less clear if H2S contributes to a decline in seagrass recruitment following large-scale seagrass die-off events. Herein, I examined tissue stable sulfur isotope signatures (d34S), belowground tissue biomass partitioning, and internal O2/H2S dynamics of newly recovering shoots over seasons at a western Florida Bay site with recurrent die-off events. Tissue results showed less H2S accumulation in tissue samples of shoots recruiting into bare sediment patches compared to tissue samples from adjacent T. testudinum and H. wrightii seagrass meadows. Additionally, internal gas dynamics of recruits showed high pO2 during the day, and no detection of meristematic H2S intrusion, despite meristem hypoxia for several hours during the night. Recruiting shoots consistently have low root biomass, likely contributing to a lack of meristem H2S intrusion, as young, minimally developed, or lack of roots in recruiting shoots limit H2S intrusion. These results lead me to suggest that high H2S levels in porewater of western Florida Bay does not limit T. testudinum recruitment into open bare patches following major die-off events, supported by the recovery, albeit slow, of this species based long-term monitoring of seagrass in the Bay.

In Essay 1, I investigate the association between CEOs’ social capital and stock price informativeness in a sample of US firms. After accounting for the fact that larger networks attract more analysts following, I find that firms with larger CEO social capital exhibit higher private information incorporation and hence more informative stock prices. Results are consistent for five different proxies for stock price informativeness. Furthermore, the positive association between social capital and informativeness is driven by more diverse networks, as measured by gender, nationality, education, or professional diversity. Overall, results suggest that private information existing in networks may result in markets that are more informationally efficient.
In Essay 2, I show that CEOs’ social capital has a positive impact on stock price informativeness in an international sample. Different robustness and endogeneity tests confirm those results. Moreover, I find that factors present at the country level can mitigate or reinforce social capital’s impact on informativeness. I consider characteristics not observable within one country that can influence such relation around the world including legal, cultural, and developmental. I uncover that for more developed countries and those with a higher quality of institutions a positive impact of social connectedness is more pronounced. In addition, I show the importance of CEOs’ connections characteristics for their impact on stock price informativeness. I find that if CEOs’ connections come from developed countries or countries that have better formal and informal institutions which affect information transparency, CEOs’ social capital becomes more important for informativeness.

John Gottman’s mathematical models have been shown to accurately predict a couple’s style of interaction using only the sentiments found in the couple’s conversations. I derived speaker sentiment slopes from 151 recorded dyadic audio conversations from the IEMOCAP dataset through an IBM Watson emotion recognition pipeline and assessed its accuracy as input for a Gottman model by comparing the cumulative speaker sentiment slope for each conversation produced from predicted emotion codes to that produced from groundtruth codes provided by IEMOCAP. Watson produced sentiment slopes strongly correlated with those produced by groundtruth emotion codes. An abbreviated pipeline was also assessed consisting just of the Watson textual emotion recognition model using IEMOCAP’s human transcriptions as input. It produced predicted sentiment slopes very strongly correlated with those produced by groundtruth. The research demonstrated that artificial intelligence has potential to be used to predict interaction quality from short samples of conversational data.