Digital Commons Network^™

Glacier Elevation Difference Data For The Conterminous U.S., Andrew G. Fountain, Bryce Allen Glenn, Christopher Mcneil, Brian Menounos

Geology Faculty Datasets

Historic and recent elevation data for the glaciers in the conterminous US were differenced to estimate glacier mass and volume change.

Historic glacier elevations were obtained from the USGS National Elevation Dataset (https://doi.org/10.15760/geology-data.04), a digitized product from the USGS 1:24,000 paper topographic maps that included the glacier extent (Gesch, 2007). Recent glacier elevations were derived from aerial lidar and from satellite radar. The lidar data were largely obtained from the USGS 3D Elevation Program (Stoker and Miller, 2022; US Geological Survey, 2024), and filtered by acquisition date using only those data collected between mid-summer and early autumn. The …

Unmasking Twitter Bots: An Applied Machine Learning Approach, Rayane El Raba’A, Layal Abu Daher

BAU Journal - Science and Technology

The rapid growth of social networks has led to increased challenges, such as fraud, cyberbullying, and the spread of automated accounts (bots). Detecting anomalies within these networks is essential to maintaining security and trust. This study explored machine learning algorithms: Random Forest, XGBoost, Support Vector Machine (SVM), and Logistic Regression for anomaly detection in social networks, specifically focusing on Twitter bot identification, By applying AI-driven data mining techniques to a dataset of 37,438 Twitter bot accounts dataset, the research evaluates the effectiveness of these models in detecting unusual patterns. XGBoost achieved the highest accuracy (84.9%), with an ROA_AUC of 0.87, …

A Mathematical Model For Managing Diphtheria Outbreaks In Nigeria Via Vaccination, Enhanced Surveillance, Effective Quarantine, And Social Distancing Measures, Hycienth Ortser Orapine, Nyiutya Cephas Ine, Dekera Jacob Washachi, Ali Audu Baidu, Lubem Matthew Kwaghkor

Mathematical Modelling and Numerical Simulation with Applications

Diphtheria is a highly infectious respiratory and skin disease that poses a significant threat to global public health. In Nigeria, children and adults with low immunity remain at high risk due to recurring annual outbreaks in the last decade, with a death rate of as high as 68.8\%. This paper presents a deterministic compartmental model to evaluate the effectiveness of interventions implemented by the Nigerian government. The model's mathematical and epidemiological validity is established through proofs of non-negativity and boundedness. The basic reproduction number ($R_0$) is derived, and stability analysis confirms that the diphtheria-free equilibrium is locally and globally asymptotically …

The Effect Of Emotional Intelligence And Logical-Mathematical Intelligence On Students’ Mathematical Problem-Solving Ability, Zulkyfli Siduppa, Sutji Rochaminah, Muh Hasbi, Dasa Ismaimuza

Jurnal Pendidikan Sains

Mathematical problem-solving ability is an essential competency in mathematics education because it supports students’ reasoning, critical thinking, and decision-making skills. This study aimed to examine the influence of emotional intelligence and logical-mathematical intelligence on the mathematical problem-solving ability of Grade XII students at SMA Negeri 1 Tolitoli. The study employed a quantitative survey design involving 143 students from the Mathematics and Natural Sciences (MIPA) program during the 2023/2024 academic year. Data were collected using an emotional intelligence questionnaire, a logical-mathematical intelligence questionnaire, and a mathematical problem-solving test on algebraic function limits. All instruments were validated and demonstrated satisfactory reliability, with …

Tumor-Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

All Works

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4^+ T cells, IFN-γ, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Hydroclimatic Whiplash Across The Contiguous United States: Characterizing Wet–Dry Transitions In Pdsi And Phdi, James Lam, Thomas Christopher Piechota

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Hydroclimatic whiplash, characterized by rapid transitions between wet and dry conditions, poses a growing challenge for water resource management and ecosystem stability. However, the spatial and statistical characteristics of wet–dry transitions across the contiguous United States are not fully understood. This study analyzed Palmer Drought Severity Index (PDSI) and Palmer Hydrological Drought Index (PHDI) records from 344 NOAA climate divisions spanning 1895–2024. Whiplash was defined as a transition between severe drought and severe wetness within 12-month rolling windows. Statistical descriptors including lag-1 autocorrelation, variance, and skewness were evaluated for whiplash and non-whiplash windows to assess differences between them. Whiplash hotspots …

Dynamical Behavior, Extinction And Persistence In A Stochastic Delayed Two-Strain Epidemic Model With A Generalized Crowley-Martin Incidence Rate, Amina Allali, Dounia Bentaleb, Saida Amine

Mathematical Modelling and Numerical Simulation with Applications

This paper develops and analyzes a novel delayed stochastic SIR epidemic model with two interacting strains and general incidence functions. By establishing the existence and uniqueness of a positive global solution, the well-posedness of the model under stochastic perturbations is ensured. Extinction occurs when the stochastic reproduction number falls below unity, as demonstrated through Itô calculus and martingale convergence theorems, while persistence is guaranteed under the Crowley–Martin incidence when it exceeds one. Numerical experiments based on the Positive Preserving Truncated Euler–Maruyama (PPTEM) scheme confirm the analytical predictions and highlight the influence of time delay and noise intensity on the long-term …

Tumor–Immune Dynamics With Memory And Time Delay: A Fractional-Order Model With Ctla-4 Regulation, Mutaz Mohammad, Mohyeedden Sweidan, Alexander Trounev, Fathalla Rihan

Mathematical Modelling and Numerical Simulation with Applications

This study develops a fractional-order tumor-immune interaction model incorporating Caputo memory effects, delayed immune activation, and CTLA-4 checkpoint regulation. The model describes the coupled dynamics of tumor cells, CD4$^{+}$ T cells, IFN-$\gamma$, and CTLA-4, and extends classical integer-order tumor-immune models by accounting for hereditary immune responses and biologically motivated latency effects. Theoretical properties, including positivity, boundedness, equilibrium structure, and fractional-order stability, are examined to establish the biological and mathematical consistency of the model. The delayed fractional system is then investigated computationally by comparing several numerical methods, including finite difference discretization, Daubechies wavelet collocation, Euler wavelet collocation, and a predictor-corrector scheme. …

Numerical Method For Strongly Variable-Density Flows At Low Mach Number: Flame-Sheet Regularisation And A Mass-Flux Immersed Boundary Method, Matheus P. Severino, Fernando F. Fachini, Elmer M. Gennaro, Daniel Rodríguez, Leandro F. Souza

Mathematical Modelling and Numerical Simulation with Applications

A low-Mach-number flow, in the laminar regime, has intrinsically two characteristic spatial scales for a given time scale, or two characteristic temporal scales for a given spatial scale, and these dual scales are very different due to the disparity between the flow and acoustic speed. Therefore, low-Mach-number flows impose mathematical and computational challenges in their description. Standard numerical methods for compressible flows, which are typically designed for problems with a single dominant spatial and temporal scale, require alternative approaches, such as preconditioning techniques or solvers tailored for low-Mach-number equations. The present work introduces a simplified fluid dynamics model for flows …

Interval-Valued Neutrosophic Dombi Bonferroni Mean Aggregation Operators In Medical Diagnosis And Sustainable Energy, Maryam Faisal, Muhammad Nadeem, Muhammad Kamran

Neutrosophic Systems with Applications

Medical diagnosis is one of the most difficult fields in which decisions must be made due to the fact that medical information often has characteristics of uncertainty, incompleteness, imprecision and even contradiction. Traditional aggregation and decision-making methods are often not well suited to such complexities, and may result in less reliable diagnostic outcomes. In order to overcome these drawbacks, the authors propose a new approach using a novel representation of Interval-Valued Neutrosophic Sets (IVNSs), the Dombi operational laws, and Bonferroni Mean (BM) aggregation operators. The proposed framework is specifically aimed at coping with uncertainty, indeterminacy and falsity all at once …

Linear Programming Model For Optimal Crop Allocation Under Varying Land Sizes In Pawaga, Tanzania, Mustapha Abbas Kiswanya, Seleman Ismail, Saul Mpeshe

Tanzania Journal of Science

Farmers often rely on intuition when selecting crops, which can lead to suboptimal profits. In this study, we examine the optimal annual crop allocation under varying land sizes for farmers in Pawaga, Iringa District, Tanzania. A parametric linear programming (LP) model is developed to determine crop allocation combinations that maximise profit subject to land, labour, and capital constraints. Unlike conventional approaches that require separate models for different farm sizes, the proposed model accommodates land holdings ranging from 1 to 9 acres and integrates nine crop types within a single framework. Data were collected over three (3) agricultural seasons from extension …

Concealed History Of Microplastic Pollution In The Msimbazi Estuary, Dar Es Salaam, Tanzania, Dativa J. Shilla

Tanzania Journal of Science

This study reconstructed the historical deposition of microplastics in sediments of the Msimbazi Estuary, Tanzania, using a radiometrically dated sediment core. Sediment chronology established using ²¹⁰Pb and the Constant Initial Concentration (CIC) model provided an approximately 83-year depositional record. The ²¹⁰Pb age model suggested marked temporal variability in sedimentation rates, with peaks around 1961, 1998, 2013, and 2015, potentially reflecting hydrological extremes and catchment disturbances. Anthropogenic microparticles were detected throughout most of the sediment core, with concentrations ranging from below detection limit (BDL) to 34 items kg⁻¹ dry weight (mean 8.5 ± 3.2 items kg⁻¹), and their abundance generally increased …

Corkscrew Motion Of Trypanosoma Brucei Is Driven By Helical Beating Of The Flagellum And Facilitated By Its Bent Shape, Sizhe Cheng, Devadyouti Das, Mykhaylo Barchuk, Raveen Armstrong, Michele M. Klingbeil, Becca Thomases, Shuang Zhou

Mathematics Sciences: Faculty Publications

Significance:

Trypanosoma brucei is a single-cell parasite causing sleeping sickness across 37 sub-Saharan countries, threatening ~60 million people. Revealing its locomotion mechanism is critical to understand its lifecycle in fly vectors and mammalian hosts; however, progress has been hindered by the rapid and complex cell deformation driven by a laterally attached flagellum. By tracking the 3D motion of fluorescent particles attached to cell surface, we demonstrate that tip-to-base helical waves generate self-propulsion like an active corkscrew, with reactive torque inducing counterrotation of the cell body. These findings inform future investigations of the underlying biomechanics, suggest potential routes for therapeutic intervention, …

Parametric Modular Answer Set Programs Made Declarative, Jorge Fandinno, Yuliya Lierler, Torsten Schaub

Computer Science Faculty Publications

In this paper, we explore the concept of modularity in first-order answer set programming (ASP). We introduce a new formalism called parametric modular logic programs, which allows defining subprograms with parameters and intensionality statements. We demonstrate how this formalism can capture the semantics of clingo-programs with collective control , a feature that enables structuring and instantiating subprograms. We provide theoretical foundations for modular ASP, illustrate its usefulness, and connect to traditional non-modular ASP.

Ai For Regression Analysis And More, Eli Snir

Generative AI Teaching Activities

Students use Copilot and NotebookLM to create a dataset and develop statistical analyses including regression.

Autoencoders As Classifiers Trained On Single Sources Of Radiographic Images For Generalizability Across Unseen Sources, Ryan M. Putney

USF Tampa Graduate Theses and Dissertations

Artificial neural networks trained to classify X-ray images according to disease state will learn to distinguish between technical or procedural variations in the images rather than features relevant to the disease. For example, the model might learn to recognize the machine that captured the image or to distinguish an image taken while the patient is lying supine or standing upright. The present work is aimed at using autoencoders as a generalizable classifier to detect COVID vs pneumonia (PNA) X-ray images. The first experiment was to find an architecture for a fully convolutional autoencoder (CAE) and for a convolutional autoencoder with …

Operationalizing Supply-Chain Hygiene In Graduate Is Education: A Hands-On Module For Secure Software And Ai/Ml Pipelines, Dominic A. Wilson

Journal of Cybersecurity Education, Research and Practice

Supply-chain attacks (including typosquatting, dependency confusion, compromised builds, dataset poisoning, and backdoored models) pose growing threats to analytics platforms central to Information Systems (IS). While frameworks like the Secure Software Development Framework (SSDF) and Supply-chain Levels for Software Artifacts (SLSA) offer guidance, IS curricula often lack accessible, infrastructure-light modules that build practical skills for mitigating these risks. This experience report presents a two-week module embedded in a graduate Secure Coding course required for a Master’s in Applied Security and Analytics degree. The module operationalizes secure development habits across both traditional software and machine learning (ML) pipelines. The module addresses a …

Distribution And Transport Of Lithium Ions At Interfaces Between Graphite And Carboxymethyl Celluloses, Zi-Jun Huang, Zhen-Ying Zheng, Hua-Wei Cao, Jian Wang, Qing-Feng Zhang, Sheng-Li Chen

Journal of Electrochemistry

Carboxymethyl cellulose (CMC) is a water-processable binder widely used for graphite anodes. However, a microscopic understanding of why the identity of CMC counterions (Li⁺/Na⁺/K⁺) strongly affects electrode performance remains limited. Here, molecular dynamics (MD) simulations are used to track Li⁺ transport accessibility across electrolyte/CMC/graphite three-phase interfaces, comparing pure CMC-Li, CMC-Na, CMC-K, and mixed-counterion CMC binders. We find that CMC-Li sustains a continuous Li⁺ transport pathway from the electrolyte through the binder phase toward graphite. In contrast, in CMC-Na and CMC-K, Na⁺/K⁺ ions preferentially enrich at the graphite/binder interface, forming …

The Impact Of Gender Sensitization On Requirements Elicitation: A Controlled Experiment, Ruthbertha Kateule, Salome Maro, Leonard Peter Binamungu

Tanzania Journal of Engineering and Technology (TJET)

Previous studies in software engineering have reported the importance of considering gender aspects in various software engineering activities, including requirements engineering, however, to the best of our knowledge, no work has investigated the impact of gender sensitisation on eliciting gender inclusive software requirements. The objective of this study was to understand the impact of gender sensitisation on software requirements elicitation. We conducted a controlled experiment using 40 undergraduate students from three different computing programs at the University of Dar es Salaam. The 40 participants were divided into 9 groups with both males and females. The participants were asked to elicit …

Analysis Of Influence Of Cable Clamp Slippage On Internal Force And Alignment Of Main Cable, Zhu Kun, Liao Dinghui, Hu Chengze, Shen Heshun, Cao Hongyou

Journal of China & Foreign Highway

To investigate the influence of cable clamp slippage on alignment and internal force of the main cable, a theoretical analysis method based on node coordinates of the main cable after cable clamp slippage was proposed in this paper. Based on segmented catenary theory, the influence of hanger inclination caused by hanger slippage on the horizontal force of the main cable was considered. The segmental cable element of the main cable was equivalently expressed as expressions of hanger tension and unstressed length. With total unstressed length kept constant as a control condition, the horizontal force of the main cable was iteratively …

Binding Energy Of Muonic Beryllium: Perturbative Versus All-Order Calculations, Shikha Rathi, Ulrich D. Jentschura, Paul Indelicato, Ben Ohayon

Physics Faculty Research & Creative Works

We compute the ground-state binding energy of muonic (Formula presented) (Formula presented) Be in two ways: first, the fully perturbative treatment of the nuclear-size effect often employed in light systems, and second, an approach that accounts for the finite-nuclear-size to all orders (and is inspired by calculations otherwise employed for heavy muonic ions). The results are compared term by term and show that both approaches agree to better than one part-per-million of the total energy. The objective of this work is twofold. The first is practical: to provide a parameterization that allows the extraction of the (Formula presented) (Formula presented) …

Trustworthy Reinforcement Learning And Communication-Efficient Multi-Agent Systems, Rui Zuo

Dissertations - ALL

The rapid proliferation of autonomous systems, such as Unmanned Aircraft Systems (UAS) transitioning to large-scale Beyond Visual Line of Sight (BVLOS) operations, demands a paradigm shift toward reliable, transparent, and resilient autonomy. While Deep Reinforcement Learning (DRL) and Multi-Agent Reinforcement Learning (MARL) have demonstrated exceptional capabilities in complex decision-making and coordination, their real-world deployment in safety-critical domains is hindered by two fundamental challenges. First, the opaque, "black-box" nature of DRL models prevents human operators from understanding and trusting the agents' underlying logic. Second, as multi-agent operations scale, they become severely constrained by the stochastic link qualities and strict bandwidth limitations …

Using Low-Frequency Vibrational Spectroscopy To Develop Analytical Profiles For Polymorphic Organic Molecular Crystals, Salvatore Zarrella

Dissertations - ALL

Low-frequency vibrational spectroscopy provides a sensitive approach for characterizing polymorphism and intermolecular dynamics in organic molecular crystals. Terahertz time-domain spectroscopy (THz-TDS) and low-frequency Raman spectroscopy probe collective lattice vibrations arising from weak intermolecular interactions, offering direct insight into crystal packing, phase stability, and structural organization that is often inaccessible to conventional diffraction and mid-infrared techniques. To interpret these modes, experimental measurements are combined with periodic solid-state density functional theory (ss-DFT), which enables vibrational assignments to specific intermolecular motions, and crystal structure prediction (CSP), which maps accessible polymorphic energy landscapes. Together, these methods form a unified experimental–computational framework for understanding molecular …

(R2153) Advanced Numerical Method For One Dimensional Burgers Equation With A Source Term, Yahaya Alassane Mahaman Nouri, Djibo Moustapha, Maman Yarodji Abdoul, Saley Bisso

Applications and Applied Mathematics: An International Journal (AAM)

This paper investigates a numerical strategy for the one-dimensional Burgers equation with a nonzero source term. Such equations arise in simplified models of transport and diffusion processes and are often used to assess the performance of numerical schemes for nonlinear evolution problems. The proposed approach combines a second-order Crank–Nicolson time discretization with a projection-based procedure that separates the nonlinear convective contribution from diffusive effects. Spatial approximation is carried out using a Chebyshev spectral collocation method, which provides high accuracy for smooth solutions with a limited number of degrees of freedom. The resulting fully discretized system is solved through an iterative …

(R2138) Analytical Approaches To Integral Equations In The Dynamics Of The Oblate Spheroidal: Earth-Sun-Satellite System, Kumari Ranjana, M. Shahbaz Ullah, M. Javed Idrisi

Applications and Applied Mathematics: An International Journal (AAM)

The deployment of artificial satellites in different gravitational environments has significantly impacted space exploration. We have introduced the study of artificial satellites under the influence of gravitational forces from oblate spheroids. To confront complex obstacles the myriads of both numerical and analytical techniques have been formulated. Among these methods, a new approach involving the integration of integral equations has been introduced. This innovative approach has been applied to study the trajectories of artificial satellites as they navigate the gravitational influence of oblate spheroids. Suitable kernels have been carefully chosen to solve the integral equations. The solutions have been found and …

From Detection To Segmentation: A Foundation Model Approach To Organoid Brightfield Image Analysis Using Sam 3, Hiren Manani

Theses - ALL

This thesis presents the first systematic application of SAM 3, a unified foundation model for promptable segmentation, to mouse small intestinal organoid brightfield microscopy image analysis. The work spans the complete pipeline from zero-shot baseline evaluation through domain-specific fine-tuning on a GPU cluster, and documents the full engineering process required to adapt a state-of-the-art foundation model to a novel biomedical imaging domain. A comprehensive literature review of over 20 papers spanning detection-based, classical segmentation, foundation model, and morphological analysis approaches identified a clear research gap that this thesis addresses. Five critical compatibility patches were developed to deploy SAM 3 on …

Constrained Neural Intelligence: Investigations Into Deep Multi-Objective Optimization, Naveed Tahir

Dissertations - ALL

Neural intelligence is identified with complex learning problems optimized over the high-dimensional, non-convex parameter spaces of deep neural networks. Solving such problems generally requires handling competing objectives and conflicting constraints. This is traditionally dealt with using heuristic methods that collapse such complexity into unconstrained, singular objectives. While computationally convenient, this invites a tradeoff against the precision and stability afforded by non-heuristic, geometry-aware approaches. This dissertation explores constrained multi-objective learning in various applied and theoretical contexts and highlights the feasibility of approximate methods as well as the necessity of exact methods. We propose a framework for equality-constrained deep learning via approximate …

Thermal Aberrations And Optical Design For Current And Future Gravitational Wave Detectors, Matthew Richard Todd

Dissertations - ALL

Since the first detection of gravitational waves in 2015, the LIGO-Virgo-KAGRA collabo- ration has detected over 300 gravitational-wave events. Planned upgrades to each detector will further improve gravitational wave sensitivity limits, pushing the frontier of multi- messenger astronomy and paving the way for next-generation detectors, like Cosmic Ex- plorer and Einstein Telescope. Though many challenges to reach sensitivity goals are being addressed, there are still cru- cial aspects of the detectors that are poorly understood and will inhibit gravitational wave detection commissioners from bringing future detectors to their design sensitivity. In par- ticular, increased circulating arm power targets will exacerbate …

Examining Nitrate Concentrations In Rainwater In The Marine Boundary Layer In St. Petersburg, Sam Irvine, Anson Kirsch, Ben Lloyd '25, Yasin Elshorbany, James Ivey

Student Research

Harmful algae blooms (HABs) are a common, natural phenomenon that occur in bodies of water. Karenia brevis is an algae species where, in excess, it can result in red tide events which deplete nutrients in the area, impacting aquatic life and releasing toxic gasses. Past research shows that K. brevis can grow through nutrient absorption in response to excess nitrate in water. This project sought to examine whether nitrates from rainfall are responsible for creating an increase in nitrogen concentrations in the water surrounding St. Petersburg which can lead to algae bloom growth. Results showed that rainwater has higher concentrations …

Multi-Modal Survival Prediction On Breast Cancer Mammograms And Enabling Multi-Institutional Collaborations With Federated Learning, Nikolas Koutsoubis

USF Tampa Graduate Theses and Dissertations

Cancer remains one of the leading causes of mortality worldwide, and machine learning is well positioned to leverage the large volumes of routinely collected clinical and imaging data to improve patient outcomes. Realizing this potential at scale requires three capabilities that current practice does not yet deliver in combination: models that integrate the multiple data modalities clinicians use, training procedures that respect institutional data-sharing constraints, and tooling that enables sharing of de-identified data when federated approaches are not sufficient. This dissertation develops methods and infrastructure addressing each of these capabilities in the context of breast cancer and oncology more broadly. …