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Development Of A Coupled Hydrological-Nitrogen Modeling Framework For Urban Stormwater Pond Analysis, Tione Grant

USF Tampa Graduate Theses and Dissertations

Urbanization impacts the hydrological and biogeochemical processes that regulate water quality in downstream ecosystems, leading to increased stormwater volume and nitrogen loads in receiving aquatic environments. Although stormwater ponds are designed as simple hydraulic storage basins, they function more like managed ecosystems within urban drainage systems, offering both hydraulic attenuation and passive nutrient treatment. However, nitrogen removal efficiency varies significantly and depends on interactions between hydrological and biogeochemical factors. This thesis details the development, calibration, validation, and sensitivity analysis of a coupled hydrological-nitrogen model framework for Aaran’s Pond, an urban stormwater pond in Hillsborough County, Florida.

A process-based water budget …

Development And Evaluation Of The East Florida Coastal Ocean Model (Efcom), Orion B. Scharton-Witmer

USF Tampa Graduate Theses and Dissertations

Florida’s Atlantic coast has abundant resources in beaches, state parks, aquatic preserves, and wildlife refuges as well as densely populated coastal cities in low lying areas that are all subjected to natural disaster threats from the oceans. There is an urgent need for a high-resolution coastal ocean circulation model for this region. Based on the application of the Finite Volume Community Ocean Model (FVCOM), an East Florida Coastal Ocean Model (EFCOM) is developed, downscaling from the deep ocean, across the continental shelf, and into the estuaries. It employs an unstructured grid with horizontal resolution varying from 3 kilometers at the …

Knowledge, Attitude, And Practice Regarding Vitamin D Among Adults In Western Libya: Assessment And Socio-Demographic Influences, Abdounasser Albasher Omar, Kawther Adaila, Mofida M. Alfaid, Abdurrahman M Sweidan, Ruqayah Kasheem, Samia Emhemmed Abadi, Abdulnabi Ali Abushita, Hafsa A. Alemam

Al-Bahir

Vitamin D deficiency is a widespread public health concern, necessitating a clear understanding of knowledge, attitudes, and practices (KAP) among populations. Therefore, this study was conducted to assess Libyan citizens' knowledge, attitude, and practice (KAP) regarding vitamin D in the western region of Libya. This study was carried out between June and September of 2021. A questionnaire was distributed across the study area and the complete responses were 314, with 62.7% were females and 37.3% were males.  The scores were calculated as percentages and statistically tested. Additionally, the impact of some socio-demographic factors (age, gender, education level, job, and salary) …

Investigation Of Air Emissions With A Local Composites Industry, Chris White

ETDs from 2020-2029

Workers at an office space raised complaints about a sweet-smelling odor directly outside their building. After an initial assessment of the surrounding businesses and their processes, it was determined that the workers were most likely exposed to styrene emissions from a nearby composites company. This investigation utilizes multiple weeks of direct readings with a photoionization device and active environmental sampling with a charcoal tube and pump to confirm the styrene exposure hypothesis. Weather trends were also recorded to determine correlation between weather and exposure to the sweet-smelling chemical. The direct readings found an exposure maximum of less than 2 ppm …

Statistical Methods For Mendelian Randomization Under Nonlinearity And Non-Normality, Mary Appah

ETDs from 2020-2029

Instrumental variable (IV) methods are widely used for estimating causal effects in observational studies where unmeasured confounding may bias traditional regression estimates. The core idea is to use a variable referred to as an instrument that is associated with the exposure of interest, independent of unmeasured confounders, and influences the outcome only through the exposure. While originally developed in econometrics, IV methods have been increasingly adopted in epidemiology and genetic research under the framework of Mendelian Randomization (MR), where genetic variants, most commonly single nucleotide polymorphisms (SNPs), serve as instruments. MR provides a powerful tool for investigating causal relationships between …

Efficient Multimodal Foundation Model Tuning For Hallucination Mitigation, Fei Zhao

ETDs from 2020-2029

Over the past few years, multimodal foundation models have achieved remarkable progress in perception and understanding. However, two challenges limit their reliability: (1) dependence on offline training, which in most real-world settings requires large volumes of labeled data and, as a result, hinders the model’s ability to adapt to new data or domains; (2) weak cross-modal grounding, which often leads to hallucinated content generation, producing descriptions that are linguistically fluent but inconsistent with the input visual evidence. This dissertation frames hallucination mitigation as an outcome of transitioning from fixed learning (static, offline fine-tuning) to adaptive, feedback-driven lifelong learning. By incorporating …

Learnable Structured Attention For Student-Aware Knowledge Distillation In Dense Prediction, Chen Liu

ETDs from 2020-2029

Dense prediction tasks, including object detection and semantic segmentation, require models to produce structured predictions and are widely used in real-world vision applications. Although deep networks have achieved strong performance on these tasks, their high computational cost limits deployment on resource-constrained systems such as autonomous-driving vehicles. Knowledge distillation (KD) addresses this issue by transferring knowledge from a large teacher model to a compact student model. However, existing distillation methods for dense prediction face greater challenges than those for classification due to the more complex task requirements. To overcome the challenges, this thesis presents a unified study of adaptive distillation for …

Quantum Engineered Mxene–Graphene–Plasmonic Nanocomposites For Next-Generation Transparent And Flexible Space Photovoltaics, Arash Vaghef-Koodehi

Turkish Journal of Chemistry

This study presents a quantum‑engineered, tricomponent heterostructure comprising Ti3C2T MXene quantum dots (2–5 nm), single‑layer graphene, and plasmonic gold nanoparticles (15 ± 3 nm) uniformly embedded within electrospun polyacrylonitrile nanofibers. This integrated architecture demonstrates a remarkable combination of optical transparency (89.3 ± 1.1%) and power conversion efficiency (19.7 ± 0.4%) under AM0 solar illumination, representing 340% enhancement over state‑of‑the‑art transparent photovoltaic devices. A multiscale computational framework, bridging density functional theory and device‑level drift-diffusion modeling, identifies optimal interlayer spacing (3.4 ± 0.1 Å) as the key to achieving 89.3% charge‑transfer efficiency. Concurrently, localized surface plasmon resonances at 532 nm generate electromagnetic …

Efficient Heterogeneous Photo-Fenton Degradation Of Azo Dyes Using The Nanocomposite Mgfe2o4/Mos2, Zahra Zeinali, Leila Ershadi Afshar, Naz Chaibakhsh, Farnaz Isapour

Turkish Journal of Chemistry

In the present work, a facile hydrothermal route was employed to synthesize the nanocomposite MgFe2O4/MoS2, which acted as a heterogeneous photo-Fenton catalyst for the degradation of a synthetic diazo dye, Acid Blue 113 (AB113). The nanocatalyst produced underwent analysis using multiple characterization techniques. The parameters influencing the oxidative reaction were statistically modeled and optimized using response surface methodology. Optimal degradation efficiency for AB113 (94.92%) was attained at pH 8.79 using 10 mg of MgFe2O4/MoS2 nanocatalyst, 1.6 mL of H2O2, and a reaction duration of 27 min. The synergistic effect resulting from the formation of electron-hole pairs renders the combined photocatalysis …

Physically Mixed Graphene Aerogel:Carbon Black (Ga:Cb)-Supported Pt Or Hydrothermally Synthesized Ga-Pt/Cb Hybrid Electrocatalysts For Pem Fuel Cells, Emi̇ne Teke Öner, Meli̇s Irmak Aydin, Merve Ni̇da Dursunoğlu, Ayşe Bayrakçeken

Turkish Journal of Chemistry

Graphene aerogels (GAs), having the superior properties of graphene and aerogel structures, are promising support materials for Pt nanoparticles to be used as electrocatalysts for PEM fuel cells. In this study, two routes were followed to synthesize GA-based electrocatalysts. In the first route, after GA synthesis it was physically mixed with carbon black (CB) by changing the mass ratios from 0% to 100% with 25% increments and then decorated with Pt nanoparticles via microwave irradiation, resulting in Pt/GA:CB electrocatalysts. In the second route, firstly, Pt/CB electrocatalyst was synthesized and then GA was hydrothermally added to the Pt/CB by changing the …

Modeling And Mitigating Stale Hardcoded Secrets In Version Control History, Katarina Valentine Capalbo

USF Tampa Graduate Theses and Dissertations

Hardcoded secret vulnerabilities remain a growing and persistent problem that can be difficult to mitigate once exposed. While there has been significant research and advancements in identifying and preventing hardcoded secrets, less attention has been given to understanding the risks of leaving these secrets behind in version control history, as well as analyzing the sanitization of hardcoded secrets. These challenges present a growing gap between the risk and ability to mitigate and sanitize existing secret exposures. This thesis begins to address this gap by formalizing a threat model for hardcoded secrets that persist in Git version control history after the …

Kernelizing Protein Interaction Languages: Spectral Approximations And Random Fourier Features, Aishik Ghosh

Master’s Dissertations

Protein-peptide interactions play an important role in many biological phenomena, spanning adaptive immunity to disease pathology. In the Sliding Window Interaction Grammar (SWING) framework, interactions are represented as sequences of biochemical tokens embedded using Doc2Vec, allowing robust generalisation to unobserved MHC alleles. However, classification remains limited to a single Euclidean feature space that is incapable of resolving binding landscapes. This dissertation develops SWING for four distinct kernel types: Gaussian, Laplacian, anisotropic (ARD), and the Spectral Mixture (SM) kernel, each approximated using scalable Random Fourier Features. The SM kernel incorporates prior knowledge about secondary structure into its spectral density as biological …

Main-Chain Ion-Pair Polybenzimidazole Membranes Enabling Reduced-Temperature Ht-Pemfc Operation (Down To 120°C), Brian C. Benicewicz, Huina Lin

Faculty Publications

Proton exchange membrane (PEM) fuel cells are promising for clean and efficient energy conversion across diverse applications. High-temperature PEM fuel cells (HT-PEMFCs) using phosphoric acid (PA)-doped polybenzimidazole (PBI) can operate up to 200°C, but at lower temperatures, water-induced PA loss often leads to performance degradation and reduced durability. In this work, we present a new class of main-chain ion-pair PBI membranes by integrating the imidazolium-biphosphate ion-pair units directly into the PBI backbone. This design results in membranes with high acid content, stronger acid-polymer interactions and stabilized acid retention. In single-cell tests, the Im+-PBI15-3 wt.% exhibits peak power densities of 0.79 …

You Mean What? Commognitive Conflict In Resolving Contextual Logarithm Problems, Endrayana Putut Laksminto Emanuel, Fatkul Anam, Radhitya Duta Pradana, Anik Kirana, Sikky El Walida

Jurnal Pendidikan Sains

Students’ approaches to solving contextual mathematics problems involving logarithms vary significantly due to differences in their prior mathematical understanding. These differences may trigger commognitive conflict during the interpretation and reasoning processes when students attempt to construct mathematical meaning from contextual situations. This qualitative study aimed to explore how commognitive conflict emerges as a mechanism of mathematical interpretation in students’ discourse while solving logarithmic contextual problems. Twenty students participated in the study and were grouped based on their performance. One student was selected as the main research subject for an in-depth analysis. The findings reveal that commognitive conflict appeared in two …

The Effect Of Project-Based Learning Model On Entrepreneurship (Pjbl-E) With A Deep Learning Approach On Students' Problem-Solving And Cognitive Skills, Ricce Oktasari, Irwandi Irwandi, Siti Darwa Suryani

Jurnal Pendidikan Sains

This study aims to examine the effect of the Entrepreneurship-based Project Based Learning (PjBL-E) model integrated with a pedagogical Deep Learning approach on students’ problem-solving skills and cognitive learning outcomes in biology learning. The novelty of this study lies in the integration of entrepreneurship values and pedagogical deep learning principles into Project Based Learning to create contextual and meaningful learning experiences. Entrepreneurship integration encourages students to develop innovative products, identify real-world problems, and apply biological concepts in practical and socio-economic contexts. Meanwhile, the pedagogical Deep Learning approach promotes reflective thinking, conceptual understanding, critical analysis, and the ability to connect theory …

Learning In Infants Using Intrinsically Motivated Goal Conditioned Reinforcement Learning, T I Darsan

Master’s Dissertations

Traditional artificial intelligence models learn by passively digesting large datasets. In contrast, human infants discover skills by actively interacting with their bodies and environments without explicit external rewards. This thesis introduces the Composer Architecture, a machine learning framework designed to mimic this autonomous, open-ended development. The Composer architecture operates in a multi-stage loop, the latent model using Contrastive Learning Through Time (CLTT) to compress high-dimensional raw data from visual, proprioceptive, and touch sensors into a low-dimensional space. To preserve data relationships and prevent topological collapse, a Softmax activation forces these latent representations to lie smoothly on a probability simplex. A …

Design And Evaluation Of A Code-Switching-Aware Multilingual Conversational Ai System Using Advanced Rag Architectures, Ashutosh Juvale

Master’s Dissertations

Conversational artificial intelligence has become the primary interface through which hundreds of millions of users in India seek information and customer support. Yet the way these users actually write and speak is fundamentally at odds with the monolingual assumptions baked into most retrieval and generation systems: they code-switch, fluidly mixing one or more of the twenty-two scheduled languages of India with English, frequently typing Indic words in the Roman script ("mera refund kab tak aayega"). Standard Retrieval-Augmented Generation (RAG) pipelines silently fail on such input — the retriever returns off-topic passages because the query and the knowledge base live in …

Telluric Correction Of M-Dwarf Stars Using Machine Learning, Sayak Rana

Master’s Dissertations

The study of M-dwarf stars is of prime scientific interest to us because of their closer habitable zones and the favorable conditions they offer for exoplanet detection. However, telluric contamination of the ground-based spectra results in sharp absorption lines, which makes their study cumbersome. Removing this contamination is necessary for estimating key stellar parameters. The central contribution is a one-dimensional Convolutional Neural Network (CNN) that retrieves the four atmospheric parameters governing telluric absorption: pressure, temperature, humidity, and airmass. These predicted parameters are passed to Telfit which produces an estimated telluric spectrum. The observed spectrum is then divided by this estimated …

The Interplay Between Deficit Spending And Financial Instability: An Examination Of Fiscal And Monetary Policy Interactions, Heidie Jean George

Doctoral Dissertations and Projects

This study investigates the impact of fiscal and monetary policy on financial stability and fragility, focusing on how government spending and deficits contribute to the economic and financial system. Drawing on Minsky’s Financial Instability Hypothesis (FIH) and Financial Fragility Hypothesis (FFH), the research examines the dynamic relationship between fiscal policy, financial stability, and economic stability. The study focuses on understanding how government spending and deficits affect the economic and financial system in context of private and public debt and policy interactions and the role of system risk factors. The study uses Principal Component Analysis (PCA), Vector Autoregression (VAR), Impulse Response …

High-Throughput Optical Analysis To Inform Design Of Electrochemical Biosensors, Nathan J. Ricks, Michael A. Pence, Monica Brachi, Shelley D. Minteer

Chemistry Faculty Research & Creative Works

Electrochemical biosensors are central to wearable diagnostics, point-of-care testing, and continuous health monitoring due to their low power requirements, compatibility with miniaturized electronics, and proven clinical impact. Despite these advantages, the development of new electrochemical biosensors remains slow, constrained by limited throughput, complex electrode–biomolecule interfaces, and challenges associated with selectivity and performance in chemically complex environments. This perspective outlines how the next generation of electrochemical biosensors can be enabled by decoupling high-throughput front-end discovery and optimization from electrochemical readouts using nonelectrochemical surrogate assays. Optical, affinity, and cell-sorting platforms, including SELEX, fluorescence-activated cell sorting, and chemically coupled fluorescence assays, allow orders-of-magnitude …

Morphodynamic Evolution Of Midnight Pass, West-Central Florida, Sydney C. Scott

USF Tampa Graduate Theses and Dissertations

This study takes advantage of a rare opportunity provided by the reopening of Midnight Pass due to two consecutive hurricanes in 2024, to examine the morphologic evolution of a newly reopened inlet and subsequent beach-inlet interactions. Historical aerial photos since the 1940s were collected and analyzed to understand the last closure of Midnight Pass, and to gain knowledge on the future of the newly reopened inlet. Time-series topography and bathymetry survey of the beach-inlet system were conducted bimonthly to quarterly for the first year and a half after the reopening, with the goals of quantifying the morphologic changes and understanding …

Adaptive Intervention Strategies In Co-Evolving Multiplex Networks: A Reinforcement Learning Approach To Real-Time Misinformation Containment, Anjali Ashokrao Bhadre Dr., Harshvardhan Prabhakar Ghongade Dr.

Northeast Journal of Complex Systems (NEJCS)

The growing transmission of misinformation via social media creates serious challenges to public health, democracy and social cohesion. To date, methods used to contain misinformation rely upon static representations of networks and set rules for interventions. In contrast, this study presents the first Multiplex Adaptive Reinforcement Intervention Network (MARIN), a framework for real-time adaptive intervention in the context of dynamic misinformation transmission using co-evolving multiplex networks and deep reinforcement learning. Unlike past studies that have assumed static network structures, MARIN has the ability to allow for dynamic changes in network topology as a result of both misinformation transmission and intervention …

Tinyvgg-Based Real-Time Degradation Classification For Adverse Driving Scenes Using A Newly Collected Iraqi Driving Dataset, Yousif N. Abbas, Matheel E. Abdulmunim, Nada H. Ali, Ismail A. Mageed

Journal of Soft Computing and Computer Applications

Environmental conditions such as low-light at night, fog scattering, glare artifacts, rain streaks, and rain smear distortions are significant issues of camera-based perception in Autonomous Vehicles (AVs). These degradations alter the statistics of the scene, mask structure, introduce non-uniform noise, and adversely affect downstream vision processes, including detection and tracking. To overcome this shortcoming, this paper presents a lightweight TinyVGG-based degradation classification system that runs in real time. The network extracts discriminative spatial features with hierarchical convolutional encoding and projects them to a lower-dimensional semantic representation with fully connected layers and a multi-class predictor based on SoftMax. In addition, a …

A Comprehensive Review Of 1d Deep Learning Approaches In Facial Analysis: Face Recognition, Landmark Detection, And Mesh Modeling, Duaa J. Al Hammami, Rehab F. Hassan

Journal of Soft Computing and Computer Applications

Facial Analysis has progressed rapidly with deep learning and its 2D image-based models, especially Convolutional Neural Networks (CNNs), which have been the most popular methods. In recent years, 1D deep learning models have gained traction in the search for efficient solutions for face recognition, facial landmark detection, and 3D face mesh modeling. 1D models encode the facial structure as sequences, curves, or temporal signals, resulting in high computational efficiency, a small memory footprint, and good interpretability, making them well-suited for real-time and edge devices. This review is a step-by-step, organized exploration of 1D deep learning analysis of the face, its …

Skin Lesion Classification Using Cnn Model And Augmented Dataset, Mohammed Nawzad Mohammed-Ramzi, Aso M. Aladdin

Journal of Soft Computing and Computer Applications

Skin cancer is a deadly disease. Skin lesion classification is a critical challenge due to its prevalent and deadly nature. Skin lesions are difficult for dermatologists to detect using eye examination, which is time-consuming and variable. A deep learning model of skin lesions classification has been proposed using a Convolutional Neural Network (CNN) trained on the HAM10000 dataset of 10,015 dermatoscopies. To improve resilience and address the dataset's extreme class imbalance, data augmentation techniques such as geometric transformations, brightness/contrast adjustments, blurring, noise addition, histogram equalization, color space alterations, and elastic deformations are used. With a carefully balanced 10% test set, …

Efficiency Improvement Of Rag Based Slm For Edge Devices, Pavan Prashanth Avanigadda

Master’s Dissertations

The increasing need to deploy language models on constrained devices has given rise to efficiency issues in retrieval-augmented generation (RAG) approaches. Although RAGs boost answers’ quality by retrieving knowledge from external sources, current methods utilize static retrieval mechanisms, resulting in unnecessary computation, higher latencies, and inefficiency in resource usage. In this work, an efficient RAG approach based on small language models (SLMs) is presented, which uses a efficient and adaptive retrieval scheme. This method dynamically changes the retrieval depth and context constrution based on the complexity of the query, using a trained MLP router whose routing decisions are learned from …

Developing A Model To Generate More Digital Data Of Indian Languages For Multilingual Applications, Arya Bagde

Master’s Dissertations

Most of India’s scheduled languages remain critically under-served by language technology because parallel (translated) text — the raw material that modern multilingual systems depend on — is extremely scarce. Back-translation can synthesise such data automatically, but its quality varies enormously, and unfiltered synthetic data can be worse than no data at all. This dissertation develops a framework that generates synthetic parallel data for four low-resource Indian languages spanning three language families and four scripts — Assamese (Indo-Aryan, Bengali script), Bodo (Tibeto-Burman, Devanagari), Manipuri (Tibeto-Burman, Bengali script) and Santali (Austroasiatic, Ol Chiki)—and introduces CASCADE, a learned multi-signal quality gate that scores …

Dynamic Property Ordering For Efficient Multi-Property Bounded Model Checking, Vivek Kumar

Master’s Dissertations

Formal verification plays a critical role in ensuring the correctness of modern hardware designs. As the complexity of digital systems increases, designs are often associated with a large number of verification properties that must be analyzed within limited computational resources. In conventional multi-property bounded model checking (BMC), all properties are verified simultaneously. While this approach enables parallel analysis, difficult properties can consume a disproportionate amount of resources, causing simpler properties to be delayed and reducing the overall efficiency of bug detection. This thesis presents dynamic property ordering techniques for efficient multi-property verification using SAT-based bounded model checking in the ABC …

Direct Minimization Of Hartree-Fock-Roothaan Energy Functionals Using Derivative-Free Optimization Algorithms: A Case Study On The Use Of Noninteger Slater-Type Orbitals, Ali̇ Bağci

Turkish Journal of Physics

This study presents an evaluation of derivative-free optimization algorithms for the direct minimization of Hartree−Fock−Roothaan energy functionals involving nonlinear orbital parameters and noninteger-order quantum numbers. The analysis focuses on atomic calculations employing noninteger Slater-type orbitals. Analytic derivatives of the energy functional are not readily available for these orbitals. Four methods are investigated under identical numerical conditions: Powell’s conjugate−direction method, the Nelder−Mead simplex algorithm, coordinate-based pattern search, and a model-based algorithm utilizing radial basis functions for surrogate-model construction. Performance analysis is first carried out using the Powell singular function, a well-established test case exhibiting challenging properties, including Hessian singularity at the …

Multi-Frequency Associative Memory For Continual Graph Learning Through Nested Optimization, Shuvam Kundu

Master’s Dissertations

Graph Neural Networks struggle to learn new tasks without forgetting old ones a problem known as catastrophic forgetting. In graph domains, this is compounded by structural shift, where newly added edges corrupt the learned representations of historical nodes even when model weights remain unchanged. We present CAM-Titans, a continual graph learning framework built around a two-buffer associative memory to address both parametric and structural forgetting. Our architecture operates across three timescales of adaptation: a slow base memory updated via ordinary gradient descent, an intermediate task buffer re-encoded after every task using the delta-rule, and a transient in-context state for rapid …