Applied Mathematics Commons^™

Complex Numbers And Phone Signals, Ava Barrett, Rebecca Brazeal, Javier Gutierrez

Student Scholar Symposium

Though widely used, complex numbers are often introduced in coursework without real-world context. Our team examines their application in electromagnetic waves and communication systems, focusing on how they help model and analyze phone signals. By connecting concepts from physics and math, we demonstrate their role in signal modulation and transmission. Through this project, we aim to highlight the practical importance of complex numbers in modern technology and provide a clearer understanding of their use in communication systems that power everyday devices like smartphones.

Advanced Soliton Structures And Elliptic Wave Patterns In A Sixth-Order Nonlinear Schrödinger Equation Using Improved Modified Extended Tanh Function Method, Mina Fahim, Hamdy Mohamed Ahmed, Islam Samir, Kamal Dib

Basic Science Engineering

In this work, a sixth–order extension of the nonlinear Schrödinger equation (NLSE) within its integrable hierarchy is investigated to model higher–order nonlinear and dispersive effects relevant to optical fiber systems and nonlinear wave propagation. By employing the Improved Modified Extended Tanh Function Method, a comprehensive family of exact analytical solutions is derived, encompassing bright and dark solitons, singular soliton structures, and singular periodic solutions. In addition, solution families expressed in terms of Jacobi elliptic functions, Weierstrass doubly periodic elliptic functions, and exponential profiles are obtained. The novelty of this study lies in extending the analytical framework of the NLSE hierarchy …

Stability Analysis Of Thermohaline Convection With A Time-Varying Shear Flow Using The Lyapunov Method, Kalin Kochnev

Honors Scholar Theses

This work applies the Lyapunov method to identify instabilities and compute the growth rate of a linear time-varying system. The linear system studied describes cold fresh water on top of hot salty water with a periodically time-varying background shear flow. A time-dependent weighting matrix is employed to construct a Lyapunov function candidate. The resulting linear matrix inequalities are discretized in time using the forward Euler method. As the number of temporal discretization points increases, the growth rate predicted by the Lyapunov method or Floquet theory, used for comparison, will converge to the same value obtained from numerical simulations. Furthermore, the …

On Sharpest Tail Bounds For Functions Of Tail Bounded Random Variables, Stephen Harrison

Mathematics & Statistics ETDs

Consider n real/complex, independent/dependent random variables with respective tail bounds and g a measurable function of the r.v.’s. Consider f the “sharpest” tail bound of g (sharpest in the sense, if f were any less, then for some X1, ..., Xn satisfying the conditions, g(X1, ..., Xn) would not satisfy the tail f). Significant research has been done to approximate f often with high accuracy. These results are often of the form, for g in this family, and tail bounds of Xk in this family, f is bounded by some f′ with high accuracy. However, the question “what would it …

Spectral Methods And Wavelets In Quantitative Finance Problems, Davood Damircheli

Theses and Dissertations

This dissertation leverages advanced spectral methods and wavelet techniques to address complex quantitative finance models, enhancing the computation of financial derivatives and risk assessments. Building on foundational studies, this research extends these methods to broader, intricate financial contexts. The first section explores fractional-order generalized Chebyshev wavelets (FOCW) applied to fractional advection equations, relevant in both mathematics and physics. Using a regularized beta function to compute the Riemann-Liouville fractional integral operator, this study introduces a novel numerical scheme with robust accuracy, confirmed through error analysis and empirical tests. The second part examines the fractional Black-Scholes equations for option pricing under subdiffusive …

Modeling Private Debt Using U.S. Consumer Expenditure Data, Stsiapan Dziamentsyeu

Honors Capstones

This project models private household debt among U.S. consumers using data from the Consumer Expenditure Survey (CES) between 2013 and 2023. The analysis focuses on identifying how demographic and economic characteristics, such as income, housing expenditures, education, and occupation, relate to non-mortgage “other” loan balances. After initial model development produced poor residual behavior due to zero-inflation from imputed debt values, the analysis was refined to include only households reporting verifiable debt. Multiple modeling techniques, including AIC-based variable selection and Lasso regularization, were compared under a five-fold cross-validation framework. The Lasso model achieved superior predictive accuracy (RMSE = 1.55, MAE = …

Math Meets Climate: The Energy Balance Model, Maria I. Sanchez Muniz

Open Educational Resources

This assignment introduces students to the mathematics of Earth’s climate through the classical energy balance model. Students analyze how incoming solar radiation, outgoing thermal radiation, and temperature-dependent albedo interact to determine Earth’s equilibrium temperature. Using analytical calculations and computational tools, students identify equilibrium states, assess their stability, and interpret the results through the lens of dynamical systems and bifurcation theory. The activity builds conceptual understanding of climate feedbacks, greenhouse effects, and tipping behavior using a transparent, one-variable model. Designed for applied mathematics and interdisciplinary STEM courses, this assignment emphasizes computation, physical interpretation, and real-world relevance. It is released as a …

Understanding Enso Through Mathematical Models, Maria I. Sanchez Muniz

Open Educational Resources

This assignment introduces students to conceptual models of the El Niño–Southern Oscillation (ENSO) and guides them through a structured investigation of their physical and mathematical foundations. Students analyze the recharge–oscillator and delayed–oscillator frameworks, explore how differential equations capture ocean–atmosphere interactions, and evaluate parameter-driven changes in oscillatory behavior. A key component of the work is the guided use of generative AI as a research tool: students employ AI models to locate peer-reviewed literature, interrogate model extensions, and refine their understanding of complex mechanisms, while synthesizing all final explanations in their own words. By blending classical climate modeling with modern AI-supported inquiry, …

A Harmonious Equation: How To Facilitate The Interdisciplinary Nature Of The Arts In A High School Math Classroom, Myah Medendorp

Honors Theses

The education system is constantly evolving and advancing, and the teaching methods should advance along with it. Musical involvement has long been cited as having a positive effect on mathematical proficiency and being an easy way to boost mathematical performance but is most often used in an elementary school setting and the early learning years. Are there ways to use this scientific relationship to boost performance and engagement in a secondary school environment?

This literature review functionalization takes a closer look at this relationship and how it can be implemented effectively in a high school math classroom beyond the young …

Global-Local Method For Poroelasticity Problems With Localized Pressure Effects, Hemantha Kunwar

Math Department Colloquium Series

In many poroelasticity applications, pressure effects are confined to a small region, making it inefficient and possibly unnecessary to solve the full system across the entire domain. Instead, we propose to solve the poroelasticity problem locally, where pressure effects are significant, and use a simpler linear elasticity model elsewhere. This creates a coupled elasticity–poroelasticity problem with transmission conditions. To solve this coupled problem, we propose a new non-intrusive global–local algorithm that iteratively solves the elasticity problem in the entire (global) domain and the poroelasticity problem only in a local domain, ensuring proper transmission conditions across the interface. This approach, which …

Extensions Of Weighted Integral Inequalities For Ga-Convex Functions In Connection With Fejér’S Result, Muhammad Amer Latif

Publications and Research

No abstract provided.

(R2136) Dimensionless Modeling And Analytical Insights Into Botanical Biofiltration Systems For Voc Reduction, N. Jeeva, K. M. Dharmalingam, M. Veeramuni

Applications and Applied Mathematics: An International Journal (AAM)

This paper investigates the mathematical modeling of volatile organic compound (VOC) removal using a botanical biofiltration system, an effective biological treatment method. It examines mass balance concentrations in the gas and biofilm phases, highlighting their significance in VOC degradation and indoor air quality enhancement. The governing equations of the proposed model are converted into a dimensionless form, resulting in second-order nonlinear differential equations. Derived from mass transfer and reaction kinetics principles, these equations incorporate specific boundary conditions to capture gas-biofilm interactions and VOC removal dynamics in biofiltration. The modified Adomian decomposition method (MADM) is utilized to derive analytical solutions for …

(R2084) Clarification Of Mathematical Criteria For Conservative Forces And Vector Fields With A Point Singularity In Engineering Studies, Nelli Aleksandrova

Applications and Applied Mathematics: An International Journal (AAM)

The paper deals with a comprehensive analysis of existing mathematical criteria for conservative vector fields including algebraic and geometrical interpretation of Extended Green’s Theorem. Special attention is devoted to the fields containing a point singularity, which represents the primary challenge both in teaching Calculus and in carrying out scientific research in general in computer engineering. Examples of real-world physical fields such as gravitational, electric and magnetic ones are used to appreciate the proposed clarification while teaching various interchangeable disciplines such as mathematics, physics and engineering. Other examples from scientific research literature are provided as well to support the necessity of …

(R2134) Continuous Shooting Approach With Improved Shooting Slope For Solving Higher Integer Order Boundary Value Problem, Razaq Adekola Oderinu, Adebowale Niyi Aderibigbe, Saheed Alao, Ahmed Adeyi Yahaya

Applications and Applied Mathematics: An International Journal (AAM)

This study presents a semi-analytical shooting method for solving nonlinear higher-order boundary value problems by integrating the Adomian Decomposition Method into the shooting technique, enabling series-form solutions. To enhance convergence, new higher-order shooting slopes and their corresponding supplementary equation formulas were introduced. Three numerical examples demonstrated the method’s accuracy: for the first two, absolute errors were computed using available exact solutions, while the third was compared with reference literature due to the absence of an exact solution. The method achieved very small absolute errors in the first two cases, and results from the third closely matched the literature. Tolerance values—defined …

(R2125) Impact Of Convective Condition And Inclined Magnetic Field On Casson Nanofluid With Bioconvection In Porous Medium, Touseef Fayaz, O. Otegbeye, Md. Sharifuddin Ansari

Applications and Applied Mathematics: An International Journal (AAM)

This paper presents an analysis on steady magnetohydrodynamic boundary layer flow of Casson nanofluid with microorganisms near an expandable boundary in a porous medium. The flow behaviour is analysed by incorporating impacts of magnetic field applied in a direction which makes an angle α∗ with the boundary and convective conditions in temperature, nanoparticle concentration and microorganism density at boundary. Non-dimensionalised nonlinear and coupled system of ordinary differential equations are solved by exercising a numerical algorithm termed as spectral local-linearization method. The considered numerical algorithm is found to be convergent and capable of yielding accurate results. Graphical sketches of solutions obtained …

The Food Truck: A Multi-Product Newsvendor With Expectile Risk, Sekyiwaah Nuamah

Electronic Theses and Dissertations

The Newsvendor Problem is a fundamental model in Operation Research and Supply Chain Management used to determine the optimal order quantity under uncertain demand to minimize expected costs.
This research extends the classical Newsvendor problem to a multi--product setting, addressing the risk of asymmetric cost structures faced by a food truck. This thesis introduces expectile risk measures to quantify and manage uncertainty in demand, moving beyond traditional risk metrics. To evaluate the impact of expectile-based decision-making, we analyze three types of demand distributions--simple, symmetric, and skewed. For skewed distributions, we apply linear spline inverse interpolation to derive expectile values from …

Deep Learning With Kalman Filter, Rexford Julius Quaye

Electronic Theses and Dissertations

This thesis presents an extension of the Kalman filter to handle nonlinear and non-Gaussian systems. The standard Kalman filter is optimal under Gaussian assumptions but struggles with more complex noise models. This work introduces a novel loss function based on the Mahalanobis distance, which incorporates the covariance structure of measurement errors, enabling the filter to adapt to non-Gaussian scenarios. The neural network framework is applied to predict the system’s process model, while retaining the classical Kalman measurement update. The proposed methodology is demonstrated through examples of car position and rocket altitude tracking. The results show that the new approach performs …

Generalizing Threshold-Based Multiparty Computation To Ramp Schemes, Christian Tucker

All Theses

Secure multiparty computation (MPC) enables multiple participants to jointly compute functions over their private inputs without revealing them. Classical threshold based protocols, such as the BGW protocol, perform computations on scalar values using (k,n)-threshold secret sharing. While these protocols provide strong security guarantees, they become computationally expensive when applied to large matrices or multiple secret values. In this work, we investigate the use of ramp schemes, secret sharing schemes that encode sets of secrets with a trade-off between privacy and efficiency, to generalize BGW computations. We show that the linear operations performed on shares (k,n)-threshold schemes in BGW can be …

Stochastic Functional Data-Driven Models For Real-Time Battery Health Forecasting Under Dynamic Operating Conditions, Joshua Owusu

Electronic Theses and Dissertations

This thesis provides an effective statistical model to predict the real-time state of lithium-ion batteries for reliable Battery Management Systems (BMS). It highlights battery data (voltage, current, temperature) as smooth functional curves. The principal method demonstrates diminishing trends to health outcomes like State of Health (SoH) and Remaining Useful Life (RUL) by employing Functional Principal Component Analysis (FPCA) and Bayesian Functional Linear Models (FLMs). The primary objective is to figure out how uncertain forecasts are. Simulations demonstrate that the highest accuracy (lowest MSE) is achieved through low noise levels along with large sample sizes. The final system provides a highly …

Coupled Machine Learning Models: Combining Observations And Numerical Analysis In A Physics-Regularized Approach, Austin B. Schmidt

LSU New Orleans Theses and Dissertations

This dissertation investigates surrogate modeling for fixed-location environmental forecasting using novel data-combination techniques. The work surveys the landscape of observational measurements and numerically generated data, identifying similar research and gaps in current methodologies. The ratio-coupled training framework is introduced to combine two data sources per predicted feature through a tunable parameter that weights training signal strength. An optimization scheme is developed to simultaneously tune surrogate weights and the coupled signal ratio, allowing relative influence between signals to act as an explicit regularizer. Three case studies demonstrate the methodology and approach in a variety of contexts. The first study is based …