Applied Mathematics Commons^™

Modeling Overwash Processes On Barrier Islands, Beth Thomas

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Mathematical Model For Wound Healing In The Reef-Building Coral Pocillopora Damicornis, Quintessa Hay, Luke Gardner, Eunice Pak, Liza M. Roger, Rebecca A. Segal, Nastassja A. Lewinski, Angela M. Reynolds, Anna Shaw

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Gaff-Msis-Robertson: Assessing Lyme Disease Dynamics With Lymesim 2.0, Holly Gaff

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Application Of Physics Informed Neural Networks For Predicting Disease Dynamics, Alonso Gabriel Ogueda, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Msis-Kadelka: Canalization Reduces The Nonlinearity Of Regulation In Biological Networks, Claus Kadelka, David Murrugarra

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling Physiological Response To Stress Via Hormone And Catecholamine Production, Helen Harris

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mathematical Model Of Competing Nest Founding Strategies By Queen Harvester Ant Pogonomyremex Californicus, Tamantha Pizarro, Yun Kang

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

The Effects Of Brain Control: A 3-D Agent-Based Model For Studying Pain, Kayla Kraeuter, Carley Reith, Benedict Kolber, Rachael Miller Neilan

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mathematical Modeling And Simulation Of Migration Dynamics Of Ukrainian Refugees Due To War In Ukraine 2022-2023, Danielle Sitalo, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling The Communication Dynamics In Human-Autonomy Teams: Insights From Search And Rescue Scenarios, Carlos E. Bustamante Orellana, Lucero Rodriguez Rodriguez, Yun Kang

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Mathematical Modeling Of The Impact Of Lobbying On Climate Policy, Andrew Jacoby, Claire Hannah, James Hutchinson, Jasmine Narehood, Aditi Ghosh, Padmanabhan Seshaiyer

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Compartmental Modeling For The Neophyte: An Application Of Berkeley-Madonna, Olcay Akman, Siddharth Bhumpelli, Cody Cline, Christopher Hay-Jahans

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Mathematical Model Of Acanthocephalan Parasite Infection In Gull-Billed Terns, Charlotte Moser

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Modeling The Opioid Epidemic: Medicated Versus Non-Medicated Treatment, Gabriel Greenberg, Maniha Akram, Muskan Agarwal

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Estimating & Modeling The Growth Of Bromeliads, Erin N. Bodine, Helen O. Udeochu, Alana G. Wells

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Convolutional Neural Network-Based Gene Prediction Using Buffalograss As A Model System, Michael Morikone

Complex Biosystems Program: Dissertations and Student Research

The task of gene prediction has been largely stagnant in algorithmic improvements compared to when algorithms were first developed for predicting genes thirty years ago. Rather than iteratively improving the underlying algorithms in gene prediction tools by utilizing better performing models, most current approaches update existing tools through incorporating increasing amounts of extrinsic data to improve gene prediction performance. The traditional method of predicting genes is done using Hidden Markov Models (HMMs). These HMMs are constrained by having strict assumptions made about the independence of genes that do not always hold true. To address this, a Convolutional Neural Network (CNN) …

Effect Of Under-Reporting, Vaccine Efficiency And Social Behavior On Epidemic Progression, Samiran Ghosh

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Fibonacci Differential Equation And Associated Spiral Curves, Mehmet Pakdemirli

CODEE Journal

The Fibonacci differential equation is defined with analogy from the Fibonacci difference equation. The linear second order differential equation is solved for suitable initial conditions. The solutions constitute spirals in the polar coordinates. The properties of the spirals with respect to the Fibonacci numbers and the differences between the new spirals and classical spirals are discussed.

Modeling And Estimation Of A Continuous Flexible Structure Using The Theory Of Functional Connections, Riccardo Bevilacqua

Math Department Colloquium Series

This talk presents a novel method for modeling and estimating the dynamics of a continuous structure based on a limited number of noisy measurements. The goal is reached using a Kalman filter in synergy with the recently developed mathematical framework known as the Theory of Functional Connections (TFC). The TFC allows to derive a functional expression capable of representing the entire space of the functions that satisfy a given set of linear and, in some cases, nonlinear constraints. The proposed approach exploits the possibilities offered by the TFC to derive an approximated dynamical model for the flexible system using the …

Nuclear Dimension Of Graph C∗-Algebras With Condition (K), Gregory Faurot, Christopher Schafhauser

Department of Mathematics: Faculty Publications

We prove that for any countable directed graph E with Condition (K), the associated graph C^*-algebra C^*(E) has nuclear dimension at most 2. Furthermore, we provide a sufficient condition producing an upper bound of 1.

Is Math Real? How Simple Questions Lead Us To Mathematics' Deepest Truths, Eugenia Cheng

Dalrymple Lecture Series

Where does math come from: from rules in a textbook? From logic and deduction? Not quite. In this talk Eugenia Cheng will argue that math comes from human curiosity - most importantly, from asking questions. Many people are discouraged from asking these questions in school, thinking they’re too simple to be taken seriously, or being told that their questions are stupid. But often, these simple-sounding questions lead to wondrous mathematical revelations. Dr Cheng will take us on a journey of discovery starting with questions like "Why does 2x3 = 3x2?" and "What's the point of maths?", leading us into research-level …

Fortifying Iot Against Crimpling Cyber-Attacks: A Systematic Review, Usman Tariq, Irfan Ahmed, Muhammad Attique Khan, Ali Kashif Bashir

Karbala International Journal of Modern Science

The rapid growth and increasing demand for Internet of Things (IoT) devices in our everyday lives create exciting opportunities for human involvement, data integration, and seamless automation. This fully interconnected ecosystem considerably impacts crucial aspects of our lives, such as transportation, healthcare, energy management, and urban infrastructure. However, alongside the immense benefits, the widespread adoption of IoT also brings a complex web of security threats that can influence society, policy, and infrastructure conditions. IoT devices are particularly vulnerable to security violations, and industrial routines face potentially damaging vulnerabilities. To ensure a trustworthy and robust security framework, it is crucial to …

Exploring Parameter Sensitivity Analysis In Mathematical Modeling With Ordinary Differential Equations, Viktoria Savatorova

CODEE Journal

This paper presents an exploration into parameter sensitivity analysis in mathematical modeling using ordinary differential equations (ODEs). Taking the first steps in understanding local sensitivity analysis through the direct differential method and global sensitivity analysis using metrics like Pearson, Spearman, PRCC, and Sobol’, we provide readers with a basic understanding of parameter sensitivity analysis for mathematical modeling using ODEs. As an illustrative application, the system of differential equations modeling population dynamics of several fish species with harvest considerations is utilized. The results of employing local and global sensitivity analysis are compared, shedding light on the strengths and limitations of each …

Deep Q-Learning Framework For Quantitative Climate Change Adaptation Policy For Florida Road Network Due To Extreme Precipitation, Orhun Aydin

I-GUIDE Forum

Climate change-induced extreme weather and increasing population are increasing the pressure on the global aging road networks. Adaptation requires designing interventions and alterations to the road networks that consider future dynamics of flooding and increased traffic due to the growing population. This paper introduces a reinforcement learning approach to designing interventions for Florida's road network under future traffic and climate projections. Three climate models and a tide and surge model are used to create flooding and coastal inundation projections, respectively. The optimal sequence of decisions for adapting Florida's road network to minimize flooding-related disruptions is solved by using a graph-based …

Are The Cans In The Store “Volume Optimized”? [Mathematics], Bukurie Gjoci

Open Educational Resources

This is one of LaGuardia’s Project Connexion STEM Team’s experiential learning activities. Project Connexion's purpose is to promote creative thinking on how to engage students in the classroom. As part of this, the STEM team developed Experiential/co-curricular activities that demonstrated to students how their work in class connects to the world around them. These activities were embedded into the syllabus to ensure the participation of all students. Each professor designed a Co-curricular activity for their courses, ensuring that the Co-curricular activity directly linked course material to the outside world.

This Calculus I Experiential Learning Project aligns with one of the …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

Rigid Body Constrained Motion Optimization And Control On Lie Groups And Their Tangent Bundles, Brennan S. Mccann

Doctoral Dissertations and Master's Theses

Rigid body motion requires formulations where rotational and translational motion are accounted for appropriately. Two Lie groups, the special orthogonal group SO(3) and the space of quaternions H, are commonly used to represent attitude. When considering rigid body pose, that is spacecraft position and attitude, the special Euclidean group SE(3) and the space of dual quaternions DH are frequently utilized. All these groups are Lie groups and Riemannian manifolds, and these identifications have profound implications for dynamics and controls. The trajectory optimization and optimal control problem on Riemannian manifolds presents significant opportunities for theoretical development. Riemannian optimization is an attractive …

A Multitask Learning Framework For Pilot Decontamination In 5g Massive Mimo, Crallet Victor

Tanzania Journal of Engineering and Technology (TJET)

Reference signals enable the acquisition of channel state information (CSI) for purposes such as channel estimation, beam selection, precoding, and symbol detection in 5G massive multiple-input multiple output (MAMIMO) systems. Eventually, as more and more users and cells are added, orthogonal reference signals become few which leads to pilot contamination. Pilot contamination limits the performance and occurs when non-orthogonal reference signals occupy time-frequency resources that are alike. Learning-based techniques have been proposed to alleviate it. However, each can only learn to perform a single task namely pilot assignment, power allocation, pilot design, or de-noising for pilot decontamination. In addition, each …

The Role Of Nanofluids In Renewable Energy Engineering, M. M. Bhatti, K. Vafai, Sara I. Abdelsalam

Basic Science Engineering

No abstract provided.

Modeling Nonsegmented Negative-Strand Rna Virus (Nnsv) Transcription With Ejective Polymerase Collisions And Biased Diffusion, Felipe-Andres Piedra

Research Symposium

Background: The textbook model of NNSV transcription predicts a gene expression gradient. However, multiple studies show non-gradient gene expression patterns or data inconsistent with a simple gradient. Regarding the latter, several studies show a dramatic decrease in gene expression over the last two genes of the respiratory syncytial virus (RSV) genome (a highly studied NNSV). The textbook model cannot explain these phenomena.

Methods: Computational models of RSV and vesicular stomatitis virus (VSV – another highly studied NNSV) transcription were written in the Python programming language using the Scientific Python Development Environment. The model code is freely available on GitHub: …