Bulletin of the American Physical Society
2021 Joint Spring Meeting of the Texas Sections of APS, AAPT and Zone 13 of the SPS
Volume 66, Number 2
Thursday–Sunday, April 8–11, 2021; Virtual
Session B08: General Physics, Data Science, and Computational Physics - II |
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Friday, April 9, 2021 6:00PM - 6:12PM |
B08.00001: A Study of Self-Organization in Small Systems with Simple Dynamics J. M. Rejcek, William J. B. Oldham, Jr. Self-organization in small systems of particles with simple dynamic laws has been simulated. For the two kinds of systems studied, the motion and the final system state for various dynamic iterations are presented. In the first system design, two kinds of particles are simulated. Like particles have a repulsive force while unlike particles have an attractive force. Initially, the particles are randomly distributed in a two dimensional square bounded region, and then allowed to dynamically interact for a number of iterations. Using the inverse square force, modified at short distances, most cases result in equilibrium with the particles paired off. In the second system design, there are two groups of particles initially separated by a boundary. Each side's particles are further divided into two groups. The forces among all of the particles can be defined to study their dynamics. PACS numbers: 02.70.-c, 02.60.Cb [Preview Abstract] |
Friday, April 9, 2021 6:12PM - 6:24PM |
B08.00002: ParaMonte::ParaDRAM - A Parallel Adaptive Markov Chain Monte Carlo Sampler in C, C$++$, Fortran, MATLAB, Python and R Shashank Kumbhare, Joshua Osborne, Fatemeh Bagheri, Amir Shahmoradi Markov Chain Monte Carlo algorithms, especially the Metropolis-Hastings algorithm, are widely used for stochastic optimization, sampling, and integration of mathematical objective functions, in the context of Machine Learning, Bayesian inverse problems, and parameter estimation. An advancement over the MH algorithm is the Delayed Rejection Adaptive Metropolis (DRAM) algorithm. Here, we present the ParaMonte software, a suite of parallel Monte Carlo optimization, sampling, and integration algorithms for Bayesian inference problems based on the concept of DRAM algorithm. The primary goal of the ParaMonte library is to streamline scientific inference by full automation and by providing runtime dynamic directions to the user. It also offers fully deterministic reproducible restart functionality of all simulations and a unified API accessible from several major scientific and Data Science programming languages, including C, C$++$, Fortran, MATLAB, Python, and R. Comprehensive automated post-processing tools integrated with the ParaMonte library also enable seamless analysis and visualization of the simulation results. [Preview Abstract] |
Friday, April 9, 2021 6:24PM - 6:36PM |
B08.00003: Enhancing Data Science Education via Artificial Intelligence James deLeon, Amir Shahmoradi, Weishu Deng In today's job market, the increasing demand for college graduates who are trained in data science spans every field of science. Therefore, university undergraduate and graduate programs must be responsive to align their curricula with the dynamic needs of the job market. However, the data scientist title is relatively new and formal data science competencies are yet to be defined. In this experiment, job descriptions from common job-posting websites are extracted algorithmically and analyzed to discover a correlation between what skills a particular field of jobs require, and the skills offered by undergraduate and graduate syllabi at the University of Texas. It will be described the determination to discover what these data science technical and soft skill competencies are by analyzing data from national job postings, along with systematically investigating how the pattern of required skills varies by domain of science, and characteristics of the job descriptions. Given such insights, programs can be prepared to identify gaps between academic preparation and the skills employers seek by identifying data science competencies to become aligned with the rapidly changing demands of the job market. [Preview Abstract] |
Friday, April 9, 2021 6:36PM - 6:48PM |
B08.00004: The Simulation of Spread of SARS-CoV-2 in a Closed Room Gajendra Gurung The SARS-CoV-2 Virus has a diameter of about 50 -140 nm, and it spread from one infected person to another via airborne droplets. SIER model has been widely used to simulate the spreading of Virus in the past but, none of them seem to quantitatively analyze the effectiveness of face-covering mask and social distancing even though they have been widely used to prevent the spread of the virus as recommended by the CDC. We want to simulate the spreading of the virus to test and quantify the effectiveness and durability of these face masks. For the simulation, we are assuming that two individuals in a 12'x12' room move about randomly using the Markov chain method, otherwise known as the random walk simulation. One of the individuals will be a carrier of Covid-19 while the other is healthy but also susceptible to infection. We assumed that both individuals are breathing regularly at the rate of 12 breathes per minute. The non-infected person is wearing a mask with quantifiable effectiveness, which will be integrated into the simulation. The initial simulation will test how long an individual can remain safe inside the room without becoming infected. Differing scenarios will be tested as well. The results of these simulations will reveal the effectiveness of social distancing and mask-wearing on a populace that follows these protocols. [Preview Abstract] |
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