Dynamic Systems Commons^™

Digital Skills And Self-Employment Among Graduates Of Technical Higher Education Institutions In Tanzania, Charles Raphael

Tanzania Journal of Engineering and Technology (TJET)

Digital skills are the driving forces for innovative, inclusive and sustainable growth through innovations and digitalization which in turn stimulate job creation, alleviate poverty, reducing inequality, facilitating the delivery of goods and services. However, large number of graduates is shown to be weak in digital skills in the industry hence affect their employability including self-employment. Recently, the Technical Higher Education Institutions (THEIs) took some initiatives to instill the digital skills to their respective students. This study therefore assessed the level of competence of digital skills among graduates following the initiatives taken by the THEIs and their influence on self-employment. It …

(R1889) Analysis Of Resonant Curve In The Earth-Moon System Under The Effect Of Resistive Force And Earth’S Equatorial Ellipticity, 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 the Moon in spherical coordinate system using the gravitational potential of Earth. 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. The effect of Earth’s equatorial ellipticity parameter …

Symmetry-Inspired Analysis Of Biological Networks, Ian Leifer

Dissertations, Theses, and Capstone Projects

The description of a complex system like gene regulation of a cell or a brain of an animal in terms of the dynamics of each individual element is an insurmountable task due to the complexity of interactions and the scores of associated parameters. Recent decades brought about the description of these systems that employs network models. In such models the entire system is represented by a graph encapsulating a set of independently functioning objects and their interactions. This creates a level of abstraction that makes the analysis of such large scale system possible. Common practice is to draw conclusions about …

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

Biology and Medicine Through Mathematics Conference

No abstract provided.

Understanding Biofilm-Phage Interactions In Mathematical Framework, Blessing Emerenini

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

Biology and Medicine Through Mathematics Conference

No abstract provided.

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

Biology and Medicine Through Mathematics Conference

No abstract provided.

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

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 that exhibit …

State Estimation—Beyond Gaussian Filtering, Haozhan Meng

LSU 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 dimensional recursive …

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

Doctoral 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 …

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.

Resilient Modulus Of Siliciclastic Unbound Granular Materials Under Repeated Wheel Loading, Siya Rimoyi

Tanzania Journal of Engineering and Technology (TJET)

The Resilient Modulus (Mr) is a fundamental material property input in modern Mechanistic-Empirical (M-E) pavements structural design. Empirical pavement design methods have been in use over a long period during which large databases of four- days-soaked California Bearing Ratio (CBR) values at or near the Maximum Dry Density have been generated in the strength assessments for Unbound Granular Materials (UGMs). In the adoption of the M-E design approach researchers have so far attempted with various degrees of success to develop models to derive resilient modulus from the conventional CBR-based databases. This study provides an extensive laboratory experimental study to investigate …

Real-Time Complex Hand Gestures Recognition Based On Multi- Dimensional Features, Isack Bulugu

Tanzania Journal of Engineering and Technology (TJET)

Gesture recognition is broadly utilized within the field of sensing. There are basically three gesture recognition methods based on computer vision, depth sensor and motion sensor. Motion sensor-based gesture recognition has few input data, fast speed, and direct access to three- dimensional information of the hand. The advantages of traditional motion sensor-based gesture recognition have gradually become a current research hotspot. The essence of traditional motion sensor-based gesture recognition is a pattern recognition problem, and its accuracy depends heavily on the feature dataset extracted from prior experience. Unlike traditional pattern recognition methods, deep learning can be used to a large …

Complete Neighbourhood Search Heuristic Algorithm For Portfolio Optimization, Collether John

Tanzania Journal of Engineering and Technology (TJET)

In portfolio optimization, the fundamental goal of an investor is to optimally allocate investments between different assets. Mean-variance optimization methods make unrealistic assumptions to solve the problem of optimal allocation. On the other hand, when realistic constraints like holding size and cardinality are introduced it leads to optimal asset allocation which differ from the mean variance optimization. The resulting optimization problem become quite complex as it exhibits multiple local extrema and discontinuities. Heuristic algorithms work well for the complex problem. Therefore, a heuristic algorithm is developed which is based on hill climbing complete (HC-C). It is utilized to solve the …

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 can …

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 connections …

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 …