Poster Presentations


Organizers:

Paul Cazeaux, University of Kansas
Agnieszka Miedlar, University of Kansas

 




Saturday, October 2, 2021 at 6:00 – 6:30 pm (CST)

 

Adeola Adeboje
University of Kansas

Lassa Fever: Controlling the Vector Agents (Rodents) Population

In this work we studied rodent population control as a way of limiting Lassa fever transmission by using predation/prey model with an addition of traps. Lassa fever is an acute viral haemorrhagic illness caused by Lassa fever virus  an  arenavirus, Among all the scenarios we considered,  increase in cats’ growth rate gave the largest reduction in rodent population while the use of traps only slightly reduced the rodent population. However, when increase in cats growth rate was combined with the use of traps, we saw a significant reduction in the rodent population.

Ahmed Al-Taweel
University of Arkansas-Little Rock

A New Upwind Weak Galerkin Finite Element Methods for Linear Hyperbolic Equations

In this work, we develop a new upwind weak Galerkin finite element scheme for linear hyperbolic equations. The upwind-type stabilizer is imposed in the scheme. An error estimate is investigated for a suitable norm. Finally, numerical examples are presented for validating the theoretical conclusions.

Katheryn Beck
University of Kansas

Spectral Element Method and the Dirac Equation

In particle physics, the Dirac Equation is a relativistic wave equation that is closely related to the Schrödinger Equation. The Dirac Equation demonstrates many of the properties of the electron including the electron spin and magnetic moment, which makes it something of interest. Our goal is to apply and investigate the Spectral Element Method (SEM) to the Dirac Equation. The SEM is one method to be able to approximate solutions to differential equations that is an expansion upon the Finite Element Method (FEM) that uses the idea from FEM of breaking the domain into smaller elements, but uses the basis functions from the Spectral Method which are usually orthogonal Chebyshev polynomials or very high order Lagrange polynomials over non-uniformly spaced nodes.

Junyao Kuang
Kansas State University

Layer Reconstruction and Missing Link Prediction of a Multilayer Network with Maximum a Posteriori Estimation

From social networks to biological networks, different types of interactions among the same set of nodes characterize distinct layers, which are termed multilayer networks. Within a multilayer network, some layers, confirmed through different experiments, could be structurally similar and interdependent. In this paper, we propose a maximum a posteriori-based method to study and reconstruct the structure of a target layer in a multilayer network. Nodes within the target layer are characterized by vectors, which are employed to compute edge weights. Further, to detect structurally similar layers, we propose a method for comparing networks based on the eigenvector centrality. Using similar layers, we obtain the parameters of the conjugate prior. With this maximum a posteriori algorithm, we can reconstruct the target layer and predict missing links. We test the method on two real multilayer networks, and the results show that the maximum a posteriori estimation is promising in reconstructing the target layer even when a large number of links is missing.

Leonard Mushunje
University of Montevallo

Quantification of Crowd Size with Mobile Phones and Social Media Big Data as a Solution to Covid19 Pandemic: Evidence from South Africa

The global pandemic of Covid19 is considered an ultimate result of the human-to-human close interactions. In most cases, it is difficult to maintain the suggested social distance measures and this is triggering the spread and incidence of the disease. As such, there is a need to insert some beautiful ways and measures to mitigate and reduce such devastating issues. This study aims to provide data-based way of measuring and quantifying the number of people in any area, mostly in developing circles. Such information helps in better dealing with the pandemic and in swift provision of emergency evacuations. We consider a selected market place and the O.R Tambo airport in Johannesburg, South Africa as our case studies. Our results suggested that there is a strong correlation between the number of people in restricted places and the captured mobile phone based social activities. The approach used and the results generated poses that big data mining and extraction from various social media platforms offers valuable and insightful patterns which helps in planning and measurements in line with the current global pandemic crisis.