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Dynamic Systems Commons

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(R1889) Effect Of Resistive Force And Earth’S Equatorial Ellipticity On Resonant Curve In The Earth-Moon System Around The Sun Using Perturbation Technique And Poincare Section, Sushil Yadav, Mukesh Kumar, Rajiv Aggarwal 2022 University of Delhi

(R1889) Effect Of Resistive Force And Earth’S Equatorial Ellipticity On Resonant Curve In The Earth-Moon System Around The Sun Using Perturbation Technique And Poincare Section, Sushil Yadav, Mukesh Kumar, Rajiv Aggarwal

Applications and Applied Mathematics: An International Journal (AAM)

In the present paper, we have determined the equations of motion of moon in spherical coordinate system using the procedure of Frick and Garber (1962). Using perturbation equations of motion are reduced to a second order differential equation. From the solution, two types of resonance are observed, (i) due to the frequencies–rate of change of earth’s equatorial ellipticity parameter and earth’s rotation rate and (ii) due to the frequencies–angular velocity of the bary-center around the sun) and earth’s rotation rate. Resonant curves are drawn where oscillatory amplitude becomes infinitely large at the resonant points. Effect ...


Developing A Miniature Smart Boat For Marine Research, Michael Isaac Eirinberg 2022 California Polytechnic State University, San Luis Obispo

Developing A Miniature Smart Boat For Marine Research, Michael Isaac Eirinberg

Computer Engineering

This project examines the development of a smart boat which could serve as a possible marine research apparatus. The smart boat consists of a miniature vessel containing a low-cost microcontroller to live stream a camera feed, GPS telemetry, and compass data through its own WiFi access point. The smart boat also has the potential for autonomous navigation. My project captivated the interest of several members of California Polytechnic State University, San Luis Obispo’s (Cal Poly SLO) Marine Science Department faculty, who proposed a variety of fascinating and valuable smart boat applications.


Universality And Synchronization In Complex Quadratic Networks (Cqns), Anca R. Radulescu, Danae Evans 2022 State University of New York at New Paltz

Universality And Synchronization In Complex Quadratic Networks (Cqns), Anca R. Radulescu, Danae Evans

Biology and Medicine Through Mathematics Conference

No abstract provided.


Understanding Biofilm-Phage Interactions In Mathematical Framework, Blessing Emerenini 2022 Rochester Institute of Technology

Understanding Biofilm-Phage Interactions In Mathematical Framework, Blessing Emerenini

Biology and Medicine Through Mathematics Conference

No abstract provided.


Effects Of Local Mutations In Quadratic Iterations, Anca R. Radulescu, Abraham Longbotham 2022 State University of New York at New Paltz

Effects Of Local Mutations In Quadratic Iterations, Anca R. Radulescu, Abraham Longbotham

Biology and Medicine Through Mathematics Conference

No abstract provided.


Bioeconomic Analysis In A Predator-Prey System With Harvesting: A Case Study In The Chesapeake Bay Fisheries, Iordanka Panayotova, Maila Hallare 2022 Christopher Newport University

Bioeconomic Analysis In A Predator-Prey System With Harvesting: A Case Study In The Chesapeake Bay Fisheries, Iordanka Panayotova, Maila Hallare

Biology and Medicine Through Mathematics Conference

No abstract provided.


Thoracoabdominal Asynchrony In A Virtual Preterm Infant: Computational Modeling And Analysis, Richard R. Foster, Laura Ellwein Fix 2022 Virginia Commonwealth University

Thoracoabdominal Asynchrony In A Virtual Preterm Infant: Computational Modeling And Analysis, Richard R. Foster, Laura Ellwein Fix

Biology and Medicine Through Mathematics Conference

No abstract provided.


Probability Distributions Of Active Sensing, Kathleen Hoffman 2022 UMBC

Probability Distributions Of Active Sensing, Kathleen Hoffman

Biology and Medicine Through Mathematics Conference

No abstract provided.


A Novel Method For Sensitivity Analysis Of Time-Averaged Chaotic System Solutions, Christian A. Spencer-Coker 2022 Mississippi State University

A Novel Method For Sensitivity Analysis Of Time-Averaged Chaotic System Solutions, Christian A. Spencer-Coker

Theses and Dissertations

The direct and adjoint methods are to linearize the time-averaged solution of bounded dynamical systems about one or more design parameters. Hence, such methods are one way to obtain the gradient necessary in locally optimizing a dynamical system’s time-averaged behavior over those design parameters. However, when analyzing nonlinear systems whose solutions exhibit chaos, standard direct and adjoint sensitivity methods yield meaningless results due to time-local instability of the system. The present work proposes a new method of solving the direct and adjoint linear systems in time, then tests that method’s ability to solve instances of the Lorenz system ...


State Estimation—Beyond Gaussian Filtering, Haozhan Meng 2022 University of New Orleans

State Estimation—Beyond Gaussian Filtering, Haozhan Meng

University of New Orleans Theses and Dissertations

This dissertation considers the state estimation problems with symmetric Gaussian/asymmetric skew-Gaussian assumption under linear/nonlinear systems. It consists of three parts. The first part proposes a new recursive finite-dimensional exact density filter based on the linear skew-Gaussian system. The second part adopts a skew-symmetric representation (SSR) of distribution for nonlinear skew-Gaussian estimation. The third part gives an optimized Gauss-Hermite quadrature (GHQ) rule for numerical integration with respect to Gaussian integrals and applies it to nonlinear Gaussian filters.

We first develop a linear system model driven by skew-Gaussian processes and present the exact filter for the posterior density with fixed ...


Statistical Applications To The Management Of Intensive Care And Step-Down Units, Yawo Mamoua Kobara 2022 The University of Western Ontario

Statistical Applications To The Management Of Intensive Care And Step-Down Units, Yawo Mamoua Kobara

Electronic Thesis and Dissertation Repository

This thesis proposes three contributing manuscripts related to patient flow management, server decision-making, and ventilation time in the intensive care and step-down units system.

First, a Markov decision process (MDP) model with a Monte Carlo simulation was performed to compare two patient flow policies: prioritizing premature step-down and prioritizing rejection of patients when the intensive care unit is congested. The optimal decisions were obtained under the two strategies. The simulation results based on these optimal decisions show that a premature step-down strategy contributes to higher congestion downstream. Counter-intuitively, premature step-down should be discouraged, and patient rejection or divergence actions should ...


Vertical Take-Off And Landing Control Via Dual-Quaternions And Sliding Mode, Joshua Sonderegger 2022 Embry-Riddle Aeronautical University

Vertical Take-Off And Landing Control Via Dual-Quaternions And Sliding Mode, Joshua Sonderegger

PhD Dissertations and Master's Theses

The landing and reusability of space vehicles is one of the driving forces into renewed interest in space utilization. For missions to planetary surfaces, this soft landing has been most commonly accomplished with parachutes. However, in spite of their simplicity, they are susceptible to parachute drift. This parachute drift makes it very difficult to predict where the vehicle will land, especially in a dense and windy atmosphere such as Earth. Instead, recent focus has been put into developing a powered landing through gimbaled thrust. This gimbaled thrust output is dependent on robust path planning and controls algorithms. Being able to ...


Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, SEYEDEH NAZANIN KHATAMI 2022 University of Massachusetts Amherst

Decision-Analytic Models Using Reinforcement Learning To Inform Dynamic Sequential Decisions In Public Policy, Seyedeh Nazanin Khatami

Doctoral Dissertations

We developed decision-analytic models specifically suited for long-term sequential decision-making in the context of large-scale dynamic stochastic systems, focusing on public policy investment decisions. We found that while machine learning and artificial intelligence algorithms provide the most suitable frameworks for such analyses, multiple challenges arise in its successful adaptation. We address three specific challenges in two public sectors, public health and climate policy, through the following three essays.

In Essay I, we developed a reinforcement learning (RL) model to identify optimal sequence of testing and retention-in-care interventions to inform the national strategic plan “Ending the HIV Epidemic in the US ...


Analysis Of Sir Epidemic Models With Sociological Phenomenon, Robert F. Allen, Katherine C. Heller, Matthew A. Pons 2022 University of Wisconsin-La Crosse

Analysis Of Sir Epidemic Models With Sociological Phenomenon, Robert F. Allen, Katherine C. Heller, Matthew A. Pons

Spora: A Journal of Biomathematics

We propose two SIR models which incorporate sociological behavior of groups of individuals. It is these differences in behaviors which impose different infection rates on the individual susceptible populations, rather than biological differences. We compute the basic reproduction number for each model, as well as analyze the sensitivity of R0 to changes in sociological parameter values.


Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel 2022 Binghamton university

Electroencephalogram Classification Of Brain States Using Deep Learning Approach, Hrishitva Patel

Computer Science Faculty Scholarship

The oldest diagnostic method in the field of neurology is electroencephalography (EEG). To grasp the information contained in EEG signals, numerous deep machine learning architectures have been developed recently. In brain computer interface (BCI) systems, classification is crucial. Many recent studies have effectively employed deep learning algorithms to learn features and classify various sorts of data. A systematic review of EEG classification using deep learning was conducted in this research, resulting in 90 studies being discovered from the Web of Science and PubMed databases. Researchers looked at a variety of factors in these studies, including the task type, EEG pre-processing ...


Sensitivity Analysis Of Basins Of Attraction For Nelder-Mead, Sonia K. Shah 2022 Bowdoin College

Sensitivity Analysis Of Basins Of Attraction For Nelder-Mead, Sonia K. Shah

Honors Projects

The Nelder-Mead optimization method is a numerical method used to find the minimum of an objective function in a multidimensional space. In this paper, we use this method to study functions - specifically functions with three-dimensional graphs - and create images of the basin of attraction of the function. Three different methods are used to create these images named the systematic point method, randomized centroid method, and systemized centroid method. This paper applies these methods to different functions. The first function has two minima with an equivalent function value. The second function has one global minimum and one local minimum. The last ...


Smoothed Bounded-Confidence Opinion Dynamics On The Complete Graph, Solomon Valore-Caplan 2022 Claremont Colleges

Smoothed Bounded-Confidence Opinion Dynamics On The Complete Graph, Solomon Valore-Caplan

HMC Senior Theses

We present and analyze a model for how opinions might spread throughout a network of people sharing information. Our model is called the smoothed bounded-confidence model and is inspired by the bounded-confidence model of opinion dynamics proposed by Hegselmann and Krause. In the Hegselmann–Krause model, agents move towards the average opinion of their neighbors. However, an agent only factors a neighbor into the average if their opinions are sufficiently similar. In our model, we replace this binary threshold with a logarithmic weighting function that rewards neighbors with similar opinions and minimizes the effect of dissimilar ones. This weighting function ...


An Adaptive Hegselmann–Krause Model Of Opinion Dynamics, Phousawanh Peaungvongpakdy 2022 Claremont Colleges

An Adaptive Hegselmann–Krause Model Of Opinion Dynamics, Phousawanh Peaungvongpakdy

HMC Senior Theses

Models of opinion dynamics have been used to understand how the spread
of information in a population evolves, such as the classical Hegselmann–
Krause model (Hegselmann and Krause, 2002). One extension of the model
has been used to study the impact of media ideology on social media
networks (Brooks and Porter, 2020). In this thesis, we explore various
models of opinions and propose our own model, which is an adaptive
version of the Hegselmann–Krause model. The adaptive version implements
the social phenomenon of homophily—the tendency for like-minded agents to
associate together. This is done by having agents dissolve ...


Dynamics Of Mutualism In A Two Prey, One Predator System With Variable Carrying Capacity, Randy Huy Lee 2022 University of North Florida

Dynamics Of Mutualism In A Two Prey, One Predator System With Variable Carrying Capacity, Randy Huy Lee

UNF Graduate Theses and Dissertations

We considered the livelihood of two prey species in the presence of a predator species. To understand this phenomenon, we developed and analyzed two mathematical models considering indirect and direct mutualism of two prey species and the influence of one predator species. Both types of mutualism are represented by an increase in the preys' carrying capacities based on direct and indirect interactions between the prey. Because of mutualism, as the death rate parameter of the predator species goes through some critical value, the model shows transcritical bifurcation. Additionally, in the direct mutualism model, as the death rate parameter decreases to ...


Convergence Properties Of Solutions Of A Length-Structured Density-Dependent Model For Fish, Geigh Zollicoffer 2021 University of Nebraska, Lincoln

Convergence Properties Of Solutions Of A Length-Structured Density-Dependent Model For Fish, Geigh Zollicoffer

Rose-Hulman Undergraduate Mathematics Journal

We numerically study solutions to a length-structured matrix model for fish populations in which the probability that a fish grows into the next length class is a decreasing nonlinear function of the total biomass of the population. We make conjectures about the convergence properties of solutions to this equation, and give numerical simulations which support these conjectures. We also study the distribution of biomass in the different age classes as a function of the total biomass.


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