Physical Sciences and Mathematics Commons^™

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Integration Of Algae And Biomass Processes To Synthesize Renewable Bioproducts For The Circular Economy, Jessica Martin

USF Tampa Graduate Theses and Dissertations

Rapid population growth and global industrialization have substantially heightened the demand for fossil-based fuels and products in various sectors of the global economy, including energy production, transportation fuels, and as raw materials for petrochemicals. The intense consumption of fossil fuels has caused immense environmental impacts, especially pertaining to carbon dioxide emissions. Shifting to renewable feedstocks (raw materials) is expected to reduce these emissions by lowering the carbon footprint of fuels and products compared to traditional fossil-derived alternatives. This transition aligns with the goal of creating a sustainable and circular economy, emphasizing efficient resource use, and reducing waste generation through recycling …

Integration Of Algae And Biomass Processes To Synthesize Renewable Bioproducts For The Circular Economy, Jessica Martin

USF Tampa Graduate Theses and Dissertations

Rapid population growth and global industrialization have substantially heightened the demand for fossil-based fuels and products in various sectors of the global economy, including energy production, transportation fuels, and as raw materials for petrochemicals. The intense consumption of fossil fuels has caused immense environmental impacts, especially pertaining to carbon dioxide emissions. Shifting to renewable feedstocks (raw materials) is expected to reduce these emissions by lowering the carbon footprint of fuels and products compared to traditional fossil-derived alternatives. This transition aligns with the goal of creating a sustainable and circular economy, emphasizing efficient resource use, and reducing waste generation through recycling …

Syntheses, Photophysics, & Application Of Porphyrinic Metal-Organic Frameworks, Zachary L. Magnuson

USF Tampa Graduate Theses and Dissertations

Porphyrins are a group of heterocyclic macrocycles that play crucial roles in various biological processes such as electron transfer, catalysis, and sensing. Hemoglobin, which carries oxygen in the blood of mammals, and chlorophyll, which drives photosynthesis in plants and algae, are both porphyrins. The ability of porphyrins to bind metal ions and their unique electronic and photophysical properties make them an excellent platform for designing functional materials for various applications, often drawing inspiration from their function in nature. Metal-organic frameworks (MOFs) are a class of porous materials that have been extensively studied in recent years due to their high surface …

Syntheses, Photophysics, & Application Of Porphyrinic Metal-Organic Frameworks, Zachary L. Magnuson

USF Tampa Graduate Theses and Dissertations

Porphyrins are a group of heterocyclic macrocycles that play crucial roles in various biological processes such as electron transfer, catalysis, and sensing. Hemoglobin, which carries oxygen in the blood of mammals, and chlorophyll, which drives photosynthesis in plants and algae, are both porphyrins. The ability of porphyrins to bind metal ions and their unique electronic and photophysical properties make them an excellent platform for designing functional materials for various applications, often drawing inspiration from their function in nature. Metal-organic frameworks (MOFs) are a class of porous materials that have been extensively studied in recent years due to their high surface …

Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera

USF Tampa Graduate Theses and Dissertations

Quantification of the true number of stained cells in specific brain regions is an important metric in many fields of biomedical research involving cell degeneration, cytotoxicology, cellular inflammation, and drug development for a wide range of neurological disorders and mental illnesses. Unbiased stereology is the current state-of-the-art method for collecting the cell count data from tissue sections. These studies require trained experts to manually focus through a z-stack of microscopy images and count (click) on a hundred or more cells per case, making this approach time consuming (~1 hour per case) and prone to human error (i.e., inter-rater variability). Thus, …

Deep Learning-Based Automatic Stereology For High- And Low-Magnification Images, Hunter Morera

USF Tampa Graduate Theses and Dissertations

Quantification of the true number of stained cells in specific brain regions is an important metric in many fields of biomedical research involving cell degeneration, cytotoxicology, cellular inflammation, and drug development for a wide range of neurological disorders and mental illnesses. Unbiased stereology is the current state-of-the-art method for collecting the cell count data from tissue sections. These studies require trained experts to manually focus through a z-stack of microscopy images and count (click) on a hundred or more cells per case, making this approach time consuming (∼1 hour per case) and prone to human error (i.e., inter-rater variability). Thus, …

Exploratory Data-Driven Models For Water Quality: A Case Study For Tampa Bay Water, Sandra Sekyere

USF Tampa Graduate Theses and Dissertations

Water, a crucial resource for sustaining life, covers approximately 70% of the earth's surface. Nonetheless, the quality of water is deteriorating rapidly due to the rapid growth of urban areas and industries, which is a worrying trend causing harm to human health and the ecosystem. Water quality forecasting has a key role in water resources management by enabling effective pollution control, ecosystem monitoring, and decision-making.

Previously, traditional statistical models were used to forecast water quality, but they were unable to examine the non-linear relationships between water quality parameters, and they assumed that all datasets were distributed normally. This study uses …

Deep Learning Enhancement And Privacy-Preserving Deep Learning: A Data-Centric Approach, Hung S. Nguyen

USF Tampa Graduate Theses and Dissertations

Deep Learning and its applications have become attractive to a lot of research recentlybecause of its capability to capture important information from large amounts of data. While most of the work focuses on finding the best model parameters, improving machine learning performance from data perspective still needs more attention. In this work, we propose techniques to enhance the robustness of deep learning classification by tackling data issue. Specifically, our data processing proposals aim to alleviate the impacts of class-imbalanced data and non- IID data in deep learning classification and federated learning scenarios. In addition, data pre-processing strategies such that dimensionality …

Characteristics And Hydraulic Behavior Of Adsorptive Media For Use In Permeable Reactive Barriers, Shelby Rocha

USF Tampa Graduate Theses and Dissertations

The Cargill ditch is located on the property of Se7en Wetlands, a 1600-ac treatment wetland system in Lakeland, Polk County, Florida. The Se7en Wetlands property was previously utilized for phosphate mining prior to the construction of the system. Nonpoint nutrient sources derived from remnants of abandoned phosphate mines – known as “legacy phosphorus” – become mobilized by stormwater runoff and impair surface water bodies by promoting harmful algal blooms (HABs). Thus, the Cargill ditch likely conveys nutrient rich flow resulting from legacy phosphorus and is thought to be one contributing factor in the occurrence of HABs within the treatment wetland …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Deep Reinforcement Learning Based Optimization Techniques For Energy And Socioeconomic Systems, Salman Sadiq Shuvo

USF Tampa Graduate Theses and Dissertations

Optimization, which refers to making the best or most out of a system, is critical for an organization's strategic planning. Optimization theories and techniques aim to find the optimal solution that maximizes/minimizes the values of an objective function within a set of constraints. Deep Reinforcement Learning (DRL) is a popular Machine Learning technique for optimization and resource allocation tasks. Unlike the supervised ML that trains on labeled data, DRL techniques require a simulated environment to capture the stochasticity of real-world complex systems. This uncertainty in future transitions makes the planning authorities doubt real-world implementation success. Furthermore, the DRL methods have …