Other Applied Mathematics Commons^™

(Si17-06) Green-Naghdi Theory Of Two-Temperature Fractional Thermoelasticity Under Thermal Shock And Ramp-Type Heating, Sagar Sankeshwari, Vinayak Kulkarni

Applications and Applied Mathematics: An International Journal (AAM)

The linear thermoelasticity theory without energy dissipation developed by Green-Naghdi has been reconstructed by using two temperature theory in the context of a time fractional derivative. In half-space, the mathematical model is discussed for one-dimensional wave propagation subject to two different problems, viz., thermal shock and ramp-type heating at the bounding surface. Assume the bounding surface is traction-free. The analytical solutions are obtained in the Laplace domain. The Gaver-Stehfest technique is numerically used to perform time domain inversion of the Laplace transform, satisfying Kuznetsov’s convergence condition. The impact of fractional order parameters on the variations of various fields inside the …

(Si17-13) A Study Of Generalized (𝜶,𝜷)-Nonexpansive Type 1 Mapping In 𝒃-Metric Space, M. J. Ali, G. Aggarwal

Applications and Applied Mathematics: An International Journal (AAM)

In this research article, we generalized KF-iteration process in 𝑏-metric space for the generalised (𝛼,𝛽)−nonexpansive type 1 mappings. We present some convergence theorems for KF-iteration process with respect to generalised mappings in 𝑏-metric space. We discussed some numerical examples of generalised (𝛼,𝛽)-nonexpansive type 1 mappings and we proved that KF-iteration process is stronger than the other iterations. The results obtained in this research generalize and improve several related results of fixed-point convergence in the literature.

(Si17-02) Integrated Mathematical Modeling And Optimization Approaches In Contemporary Engineering And Economic Applications, D. Christo Stalin, Ilakkia S., A. Parameshwari, Sahaya Brainy J., Aman Jolly, Vikas Pandey

Applications and Applied Mathematics: An International Journal (AAM)

This paper offers a calibrated synthesis of integrated modeling and optimization methods central to modern engineering and economic problems.We scrutinize the convergence of numerical methods, operations research, fuzzy-logical systems, and mathematical economics, demonstrating how these components conjointly illuminate complicated, real-world phenomena. Through an exhaustive review of prevailing frameworks, the investigation emphasizes the strategic role of stochastic modeling, queuing architecture, and optimization curricula within the twin arenas of system engineering design and economic construction. Empirical confrontation reveals that the synergistic coupling of distinct mathematical branches exponentially enhances our ability to resolve tightly-knit, largescale systems. Application re-routing is systematically demonstrated first on …

(Si17-16) Approximate Solution Of Fredholm Integral Equations Using Szász-Mirakyan Polynomials, Ruchi S. Rajawat, Shikha Pandey

Applications and Applied Mathematics: An International Journal (AAM)

This manuscript examines a family of Szász-Mirakyan operators that approximate functions on the unbounded interval [0,∞). Furthermore, we examine the discretization method for Fredholm integral equations of the first and second kind based on Szász-Mirakyan operators.We offer numerical techniques and estimate the error bound of the solution in this direction. Finally, we demonstrate numerical analysis showing the effectiveness of the newly developed algorithm constructions in the domain of one-dimensional approximation.

A Simulation Assessment Of The 'Law Of One Price', Caleb Wilkins

Computational and Data Sciences (MS) Theses

The ‘law of one price’ is an appealing notion regarding pricing of tradeable commodities that are priced in different currencies. It states that the prices of the same good in different markets should be equal after adjustment for exchange rates and that equality should persist through exchange rate fluctuations.

My research simulates the market conditions that should precipitate the ‘law of one price.’ Data was obtained from the simulated trade between algorithmic artificial intelligence agents that operated under induced boundedly rational market behaviors. Trade took place in two initially separate markets, a high-price market with a higher equilibrium price and …

A Categorical Framework For Modeling Genetic Drift, Taylor G. Mendes

Rose-Hulman Undergraduate Mathematics Journal

Genetic drift describes changes in allele frequencies that arise from chance sampling in finite populations. This paper develops a categorical framework for organizing the structural features of drift. Population states are modeled as objects, evolutionary transitions as morphisms, reversible transitions as groupoid morphisms, and structure-preserving comparisons between models as functors. Group actions are used to describe deterministic evolutionary operators such as mutation and selection, while orbits and fixed points identify reachable allele-frequency states and stable absorbing outcomes. Universal properties are then used to describe drift as a coherence condition connecting stochastic transitions with deterministic evolutionary maps. The resulting framework complements …

What We Know About Accounting Ratios: Methodological Considerations, Wojciech Kuryłek, Oskar Kowalewski

Studia i Materiały Wydział Zarządzania Uniwersytet Warszawski

Purpose: This paper provides a comprehensive literature review of the methodological aspects of financial ratio analysis, consolidating dispersed knowledge on the computation, statistical properties, and appropriate usage of accounting ratios.

Design/Methodology/Approach: The study adopts a narrative literature review methodology, systematically surveying published research on financial ratio distributions, normality testing, data transformations, outlier handling, the proportionality assumption, dimensionality reduction techniques, compositional data analysis, and recommended ratio sets for corporate financial research.

Findings: Financial ratios predominantly deviate from normal distributions, exhibiting skewness, excess kurtosis, and sensitivity to outliers. Transformation techniques such as logarithmic, square root, and Box‑Cox methods yield mixed results in …

Cdt-1d Cnn Integration With Simpson-Sobolev Regularization For High-Frequency Options Trading: With Fem-Based Heston Option Pricing, Daniel M. Margolis, Johannes Tausch, Arthur K. Selender

Mathematics Theses and Dissertations

This dissertation presents a computational framework for high-frequency options trading that combines Cross-Data-Type 1-D Convolutional Neural Networks (CDT-1D CNN) with Simpson-Sobolev regularization for directional prediction, and finite element methods (FEM) for realistic option pricing during backtesting. The core innovation lies in developing a mathematically rigorous regularization approach that maintains the adaptability of modern deep learning while enabling accurate evaluation through stochastic volatility models. The primary contribution is the Simpson-Sobolev regularization scheme, which extends traditional Sobolev regularization by incorporating Simpson’s rule for numerical integration. This approach achieves higher-order accuracy in approximating the Sobolev norms that control function smoothness. Simpson’s rule attains …

(R2140) Verifying The Proper Efficiency In Multi-Objective Problems, Javad Bavali, Hadi Basirzadeh

Applications and Applied Mathematics: An International Journal (AAM)

Over the past decade, numerous approaches have been put forward to address multi-objective optimization problems-focusing on approximating proper efficient solutions instead of the efficient solutions. These methods are important because, according to Geoffrion’s conjecture, proper non-dominated solutions constitute a dense subset of the non-dominated solution set. In the current study, first, using the definition of Kuhn-Tucker’s proper efficiency, we present a linear subproblem that investigates the proper efficiency of a randomly selected feasible point. Additionally, the relationship between the different types of solutions to this sub-problem and the proper efficient solutions of the main problem has been analyzed. Next, we …

Bias, Structure, And Inference In Applied Network Analysis, Anna Vasenina

Dartmouth College Ph.D Dissertations

This dissertation develops mathematical and statistical methods for extracting reliable information from network data across biological applications, with an emphasis on understanding what observed network structure can and cannot resolve. The first study leverages protein–protein interaction network topology in the c-di-GMP signaling system of Pseudomonas fluorescens, showing that node centrality measures accurately classify protein domain types and that physical interaction structure contributes statistically significant predictive power for biofilm formation phenotypes across nearly 200 environments, while gene expression does not. The second study examines sampling bias in lemur-plant trophic interaction networks in Madagascar, demonstrating that differential detection of diurnal versus …

Parameter Sensitivity, Identifiability, And Estimation For A Data-Driven Model Of Malaria, Katharine Gurski, Kathleen Hofman

Biology and Medicine Through Mathematics Conference

No abstract provided.

Momentum Space Algorithm For Electronic Structure Of Double-Incommensurate Trilayer Graphene, Kenneth Silver Beard

LSU Doctoral Dissertations

Numerical algorithms for computing the electronic structure of incommensurate 2D-materials using ab initio models are critical for predicting material properties and guiding experiments. For bilayers, momentum space and continuum models have been introduced to approximate observables of ab initio tight-binding models using a momentum description, despite the lack of periodicity in the tight-binding model required for Bloch theory. A similar structure has been introduced for double-incommensurate trilayers using a continuum model, where the three lattices are mutually incommensurate. However, this description leads to a four-dimensional lattice space, and numerical convergence of the density of states has been observed to be …

A New Approach To Generate Combinatorial Patterns In Logical Analysis Of Data And Its Application To Predict College Retention, Salihah Ahmed E. Jaafari

Theses and Dissertations

Student retention and degree completion remain central challenges for higher-education institutions, with significant implications for student success, institutional effectiveness, and public accountability. While advances in predictive analytics have enabled earlier identification of students at risk of withdrawal, many commonly used machine learning approaches suffer from limited interpretability, constraining their practical usefulness for advising, intervention, and policy decision making. This dissertation addresses the problem of predicting student persistence by developing and evaluating optimization based, interpretable classification models within the Logical Analysis of Data (LAD) framework. Building on existing LAD formulations, this research introduces two novel pattern generation models, the Best Term …

Decision Making For Large-Scale Problems Under Uncertainty And Conflict, Benjamin J. Hamlin

All Dissertations

Large-scale decision-making problems appear in many areas including long-range forecasting such as energy generation forecasting. Many such problems are subject to conflicting objectives and uncertain data, and can be modeled as linear optimization problems. We study novel theoretical results and algorithms for large-scale linear decision problems under conflict and uncertainty. First, we propose a parametric Benders decomposition algorithm for solving large-scale linear optimization problems with multiple objectives or deterministically uncertain objectives. Second, we extend the parametric Benders decomposition to a multi-stage setting, developing a parametric stochastic dual dynamic programming algorithm, which enables decision-making when conflicts and uncertainty have planning impacts …

How To Effectively Trap Invasive Crayfish: A Discrete Life Stage Mathematical Model, Rini Pattison

Seaver College Research And Scholarly Achievement Symposium

The red swamp crayfish is an invasive species introduced into several streams within the Santa Monica Mountains (SMM). Crayfish predation decimates native aquatic species. The Mountains Restoration Trust (MRT) has worked to remove crayfish through regular trapping in Malibu Creek.

A prior student created a crayfish life cycle model with trapping, which we expand to better predict the efficacy of crayfish removal efforts in the SMM. We separate crayfish based upon life stages and sizes. We construct and parameterize this discrete crayfish population model with and without trapping. We use literature and crayfish removal data from MRT to fit the …

Estimation Of Net Premium For Flight Delay Insurance Using The Aggregate Loss Model: A Case Study Of Indonesia Otas, Azka Nurul Husna, Yulial Hikmah, Ira Rosianal Hikmah

Jurnal Vokasi Indonesia

Air transportation, as one of the most chosen transportation modes, is frequently susceptible to delays. Flight Delay Insurance offers a vital solution to mitigate the financial losses associated with this risk. Premium pricing is a key factor influencing the decision to purchase this insurance, particularly on Online Travel Agent (OTA) platforms where product offerings are often highly comparable. The aggregate loss method is employed herein to ascertain the net premium (or pure premium) price. The loss severity component (X) is modeled using an empirical distribution, while the loss frequency component (N) is modeled using a Negative Binomial distribution with parameters …

A Probabilistic Modeling Analysis Of The Longitudinal Immune Response To Infection And Vaccination Across Demographic Groups And Pulmonary Symptoms, James O'Hanlon, Kaitlyn Sullivan, Lyndsey M. Muehling, Glenda Canderan, Jie Sun, Judith A. Woodfolk, Jeffrey M. Wilson, Rayanne A. Luke

Spora: A Journal of Biomathematics

Antibody and cytokine kinetics describe the dynamic response to immune events such as infection and vaccination. These dynamics are not fully understood, and mathematical characterization may help explain variability across demographic groups and pulmonary symptoms post-acute infection. We fit time-dependent probability models to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) data to obtain distributions of longitudinal antibody response and cytokine values. To assess differences between groups, an overlap metric is applied to the modeled response curves. Our antibody models suggest significant differences between male and female populations and demonstrate deficient antibody responses of less-healthy groups such as smokers. Our cytokine …

Optimal Intervention Strategies In Age-Structured Sirvd Model: Epidemiological And Economic Analysis Of Covid-19 In Korea, Joon Chun, Melisa Hendrata

Spora: A Journal of Biomathematics

This study employed an age-structured Susceptible-Infected-Recovered-Vaccinated-Deceased (SIRVD) model to design optimal COVID‑19 intervention strategies by integrating epidemiological dynamics with economic costs. Using empirical data from South Korea, we estimated transmission parameters and analyzed quarantine-based non-pharmaceutical interventions (NPIs) alongside targeted vaccination scenarios. Our results indicated that a stronger form of mild NPI (upper range of Level I), when combined with targeted vaccination that prioritized seniors, achieved substantial reductions in infection peaks and overall economic burden. These findings underscore the effectiveness of age-specific strategies in epidemic management and offer critical insights for policymakers seeking balanced, resource-efficient approaches.

Inhomogeneous Branching Random Walks: Incorporating Genealogy And Density Effects, Lauren Ajax, Beatrice Durham, Pratima Hebbar, Cade Johnston, Jiayi Zhang

Spora: A Journal of Biomathematics

We introduce a novel framework using inhomogeneous branching random walks (BRWs) to model biological processes, specifically by introducing genealogy-dependence in branching rates and displacement distributions to model bacterial colony growth. Current stochastic models often either assume independent and identical behavior of individual agents or incorporate only spatiotemporal inhomogeneity, ignoring the effect of genealogy-based inhomogeneity on the long-time behavior of these processes. Such asymptotics are of independent mathematical interest and are crucial in understanding the emergence of patterns. We propose several inhomogeneous BRW models in 2D space where displacement distributions and branching rates vary with time, space, and genealogy. A combined …

Liutex - A Fluid Vortex, Oscar Alvarez

Mathematics Dissertations

Fluid vortices are found everywhere in our universe. A vortex can take the form of almost anything - from the classical spiral vortex to chaotic plumes. Defining a vortex physically and mathematically is absolutely necessary if we desire to study vortices and their interactions with each other as well as our physical world. Fluid vortices are incredibly important in the study of turbulent flows. From determining wear, optimizing design for better flow, efficiency, etc., to even predicting the weather on Earth or other planets, having the ability to measure vortices in fluid flow is invaluable. In this study, I investigate …