Applied Mathematics Commons^™

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 …

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 …

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 …

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: …

Privacy-Preserving Federated Learning, Dumindu Samaraweera

Math Department Colloquium Series

AI's applicability across diverse fields is hindered by data sensitivity, privacy concerns, and limited training data availability. Federated Learning (FL) addresses this challenge by enabling collaborative machine learning while preserving data privacy. FL allows clients to engage in model training with their local data, avoiding centralized storage. However, even with FL, security threats persist, jeopardizing model integrity and client data privacy. In this presentation, we will explore our latest findings in this area of research, safeguarding sensitive data from attacks through techniques like secure multiparty computation, homomorphic encryption, and differential privacy within the FL framework, enhancing data protection, and expanding …

A Low-Complexity Algorithm To Determine Spacecraft Trajectories, Sirani Perera

Math Department Colloquium Series

The growing traffic within the Cislunar region has created a need for computationally effective methods to obtain the trajectories of spacecraft in the Cislunar region. By developing algorithms with low time and arithmetic complexities, we can effectively address these needs.

In this talk, we will present a mathematical model that uses interpolation and boundary conditions to obtain trajectories for satellites based on the principles of three-body dynamics. Following the model, we propose a low- complexity algorithm to generate satellite trajectories. Once the algorithm is proposed, we will apply it to the relevant periodic orbits in the Cislunar region. Finally, we …

On Subset Labelings Of Trees, Gary Chartrand, Ebrahim Salehi, Ping Zhang

Mathematical Sciences Faculty Research

For a nontrivial graph G, a subset labeling of G is a labeling of the vertices of G with nonempty subsets of the set [r]={1, 2, ... , r} for a positive integer r such that two vertices of G have disjoint labels if and only if the vertices are adjacent. The subset index ρ(G) of G is the minimum positive integer r for which G has such a subset labeling from the set [r]. If T is a tree of diameter d, then ρ(Pd+1) ≤ ρ(T). It is shown that there are several classes of trees T of diameter …

An Implementation Of The Method Of Moments On Chemical Systems With Constant And Time-Dependent Rates, Emmanuel O. Adara, Roger B. Sidje

Northeast Journal of Complex Systems (NEJCS)

Among numerical techniques used to facilitate the analysis of biochemical reactions, we can use the method of moments to directly approximate statistics such as the mean numbers of molecules. The method is computationally viable in time and memory, compared to solving the chemical master equation (CME) which is notoriously expensive. In this study, we apply the method of moments to a chemical system with a constant rate representing a vascular endothelial growth factor (VEGF) model, as well as another system with time-dependent propensities representing the susceptible, infected, and recovered (SIR) model with periodic contact rate. We assess the accuracy of …

Forecasting Economic Growth And Movements With Wavelet Transform And Arima Model, Omar Alsinglawi, Omar Alsinglawi, Mohammad Aladwan, Mohammad Aladwan, Saddam Alwadi, Saddam Alwadi

Applied Mathematics & Information Sciences

This study uses historical data and modern statistical models to forecast future Gross Domestic Product (GDP) in Jordan. The Wavelet Transformation model (WT) and Autoregressive Integrated Moving Average (ARIMA) model were applied to the time series data and yielded a best-fitting result of (2,1,1) for estimating GDP between 2022-2031. The study concludes that GDP is expected to increase with a positive growth rate of around 3.22%, and recommends government agencies to monitor GDP, strengthen existing policies, and adopt necessary economic reforms to support growth. Additionally, the private sector is encouraged to enhance production tools to achieve economic growth that benefits …

Neutrosophic Adaptive Lsb And Deep Learning Hybrid Framework For Ecg Signal Classification, Abdallah Rezk, Ahmed S. Sakr, H. M. Abdulkader

Applied Mathematics & Information Sciences

This paper proposes a novel hybrid framework for ECG signal classification and privacy preservation. The framework includes two phases: the first phase uses LSTM+CNN with attention gate for ECG classification, while the second phase utilizes adaptive least signal bit with neutrosophic for hiding important data during transmission. The proposed framework converts data into three sets of degrees (true, false, and intermediate) using neutrosophic and passes them to an embedding layer. In the sender part, the framework hides important data in ECG signal as true and false degrees, using the intermediate set as a shared dynamic key between sender and receiver. …

Compatibility Of Clique Clustering Algorithm With Dimensionality Reduction, Ug ̆Ur Madran, Duygu Soyog ̆Lu

Applied Mathematics & Information Sciences

In our previous work, we introduced a clustering algorithm based on clique formation. Cliques, the obtained clusters, are constructed by choosing the most dense complete subgraphs by using similarity values between instances. The clique algorithm successfully reduces the number of instances in a data set without substantially changing the accuracy rate. In this current work, we focused on reducing the number of features. For this purpose, the effect of the clique clustering algorithm on dimensionality reduction has been analyzed. We propose a novel algorithm for support vector machine classification by combining these two techniques and applying different strategies by differentiating …

The Influence Of Supply Chain Management Strategies On Organizational Performance In Hospitality Industry, Omar Jawabreh, Abdullah Mahfoud Baadhem, Basel J. A. Ali, Anas Ahmad Bani Atta, Anis Ali, Fahmi Fadhl Al- Hosaini

Applied Mathematics & Information Sciences

The studys primary goal is to analyze the connection between SCM practices and organizational performance, and it also aims to evaluate the moderating role of management type. Quantitative data collected from Jordans hotel and restaurant workers via questionnaire. Structural equation modeling is used to examine the hypothesized relationships. Organizational Performance is positively impacted by effective information sharing. Information Quality (IQ) positively affects Organizational Performance (OP), and Strategic Supplier Partnerships (SSP) play a crucial role. Customer Relationship Management (CRM) had no discernible effect on OP, according to the study. OP is positively impacted by Postponement (POS) techniques. When implemented, postponement increases …

The Effect Of System Quality And User Quality Of Information Technology On Internal Audit Effectiveness In Jordan, And The Moderating Effect Of Management Support, Ahmad Yahiya Ahmad Bani Ahmad (Ayassrah), Anas Ahmad Mahmoud Bani Atta, Hanan Ali Alawawdeh, Nawaf Abdallah Aljundi, Amer Morshed, Saleh Amin Dahbour

Applied Mathematics & Information Sciences

The goal of this study is to ascertain the moderating role that management support has in internal audit effectiveness in Jordan, as well as the impact of system quality and user quality of information technology. There were 172 responders in all, and they were split across Jordanian auditors. In the data analysis process, the quantitative analysis test— which consists of the validity test, reliability test, test of conventional assumptions, and hypothesis test—is applied. Information technology system and user quality are independent variables in this study. The dependent variable in this study is internal audit effectiveness, and the moderating variable is …