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Introduction to Volume 11 Issue 2
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 11 Issue 2 — April 2020
Contents
Using Molecular Visualization as a Tool for Culturally Competent and Culturally Relevant Teaching: A Guided-Inquiry Biochemistry Activity
Pumtiwitt McCarthy, Richard Williams, Cleo Hughes-Darden, Roni Ellington, Paminas Mayaka, Monica Jackson, and Asamoah Nkwantapp. 2–6
https://doi.org/10.22369/issn.2153-4136/11/2/1BibTeX
@article{jocse-11-2-1,
author={Pumtiwitt McCarthy and Richard Williams and Cleo Hughes-Darden and Roni Ellington and Paminas Mayaka and Monica Jackson and Asamoah Nkwanta},
title={Using Molecular Visualization as a Tool for Culturally Competent and Culturally Relevant Teaching: A Guided-Inquiry Biochemistry Activity},
journal={The Journal of Computational Science Education},
year=2020,
month=apr,
volume=11,
issue=2,
pages={2--6},
doi={https://doi.org/10.22369/issn.2153-4136/11/2/1}
}
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The central dogma is a key foundational concept in biochemistry. The idea that DNA mutations cause change at the protein level can be abstract for students. To provide a real-world example of the effect of mutation on protein function, a molecular visualization module was developed and incorporated into two biochemistry courses. This inquiry-based activity explored the molecular basis and cultural relevance of sickle cell anemia. Hemoglobin structural changes from the disease were examined. Participants used free tools including NCBI, RCSB PDB, LALIGN and Swiss PDB DeepView protein visualization software from EXPASY. This module was an active, engaging exercise which exposed students to protein visualization and increased cultural awareness.
The State of Undergraduate Computational Science Programs
Steven I. Gordon and Katharine Cahillpp. 7–11
https://doi.org/10.22369/issn.2153-4136/11/2/2BibTeX
@article{jocse-11-2-2,
author={Steven I. Gordon and Katharine Cahill},
title={The State of Undergraduate Computational Science Programs},
journal={The Journal of Computational Science Education},
year=2020,
month=apr,
volume=11,
issue=2,
pages={7--11},
doi={https://doi.org/10.22369/issn.2153-4136/11/2/2}
}
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A number of efforts have been made to introduce computational science in the undergraduate curriculum. We describe a survey of the undergraduate computational science programs in the U.S. The programs face several challenges including student recruitment and limited faculty participation in the programs. We describe the current state of the programs, discuss the problems they face, and discuss potential short- and long-range strategies that might address those challenges.
Development of a Molecular Model for Understanding the Polymer-metal Interface in Solid State Pumps
Jaime D. Guevara, Matthew L. Jones, Peter Müllner, and Eric Jankowskipp. 12–22
https://doi.org/10.22369/issn.2153-4136/11/2/3BibTeX
@article{jocse-11-2-3,
author={Jaime D. Guevara and Matthew L. Jones and Peter M\"{u}llner and Eric Jankowski},
title={Development of a Molecular Model for Understanding the Polymer-metal Interface in Solid State Pumps},
journal={The Journal of Computational Science Education},
year=2020,
month=apr,
volume=11,
issue=2,
pages={12--22},
doi={https://doi.org/10.22369/issn.2153-4136/11/2/3}
}
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Medical micropumps that utilize Magnetic Shape Memory (MSM) alloys are small, powerful alternatives to conventional pumps because of their unique pumping mechanism. This mechanism—the transfer of fluid through the emulation of peristaltic contractions—is enabled by the magneto-mechanical properties of a shape memory alloy and a sealant material. Because the adhesion between the sealant and the alloy determines the performance of the pump and because the nature of this interface is not well characterized, an understanding of sealant-alloy interactions represents a fundamental component of engineering better solid state micropumps in particular, and metal-polymer interfaces in general. In this work we develop computational modeling techniques for investigating how the properties of sealant materials determine their adhesive properties with alloys. Specifically, we develop a molecular model of the sealant material polydimethylsiloxane (PDMS) and characterize its behavior with a model Ni-Mn-Ga surface. We perform equilibrium molecular dynamics simulations of the PDMS/Ni-Mn-Ga interface to iteratively improve the reliability, numerical stability, and accuracy of our models and the associated data workflow. To this end, we develop the first model for simulating PDMS/Ni-Mn-Ga interfaces by combining the Optimized Potentials for Liquid Simulations (OPLS) [21] force field with the Universal Force Field [5], and show promise for informing the design of more reliable MSM micropumps. We also reflect on the experiences of Blue Waters Supercomputing intern Guevara (the first author) to identify key learning moments during the one-year internship that can help guide future molecular simulation training efforts.
Using Blue Waters to Assess Tornadic Outbreak Forecast Capability by Lead Time
Caroline MacDonald and Andrew Mercerpp. 23–28
https://doi.org/10.22369/issn.2153-4136/11/2/4BibTeX
@article{jocse-11-2-4,
author={Caroline MacDonald and Andrew Mercer},
title={Using Blue Waters to Assess Tornadic Outbreak Forecast Capability by Lead Time},
journal={The Journal of Computational Science Education},
year=2020,
month=apr,
volume=11,
issue=2,
pages={23--28},
doi={https://doi.org/10.22369/issn.2153-4136/11/2/4}
}
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Severe weather outbreaks come with many different hazards. One of the most commonly known and identifiable outbreaks are those with tornadoes involved. There has been some prior research on these events with respect to lead time, but shifts in model uncertainty by lead time has yet to be quantified formally. As such, in this study we assess tornado outbreak model uncertainty by lead time by assessing ensemble model precision for outbreak forecasts. This assessment was completed by first identifying five major tornado outbreak events and simulating the events using the Weather Research and Forecasting (WRF) model at 24, 48, 72, 96, and 120-hours lead time. A 10-member stochastically perturbed initial condition ensemble was generated for each lead time to quantify uncertainty associated with initialization errors at the varied lead times. Severe weather diagnostic variables derived from ensemble output were used to quantify ensemble uncertainty by lead time. After comparing moment statistics of several convective indices, the Energy Helicity Index (EHI), Significant Tornado Parameter (STP), and Supercell Composite Parameter (SCP) did the best job of characterizing the tornadic outbreaks at all lead times. There was good consistency between each case utilizing these three indices at all five lead times, suggesting outbreak model forecasting confidence may be able to extend up to 5 days for major outbreak events. These results will be useful for operational use by forecasters in forecast ability of tornadic events.
Improvement of the Evolutionary Algorithm on the Atomic Simulation Environment Though Intuitive Starting Population Creation and Clustering
Nicholas Kellas and Michael N. Grovespp. 29–35
https://doi.org/10.22369/issn.2153-4136/11/2/5BibTeX
@article{jocse-11-2-5,
author={Nicholas Kellas and Michael N. Groves},
title={Improvement of the Evolutionary Algorithm on the Atomic Simulation Environment Though Intuitive Starting Population Creation and Clustering},
journal={The Journal of Computational Science Education},
year=2020,
month=apr,
volume=11,
issue=2,
pages={29--35},
doi={https://doi.org/10.22369/issn.2153-4136/11/2/5}
}
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The Evolutionary algorithm (EA), on the Atomic Simulation Environment (ASE), provides a means to find the lowest energy conformation molecule of a given stoichiometry. In this study we examine the ways in which the initial population of molecules affect the success of the EA. We have added a set of rules to the way in which the molecules are created that leads to more chemically relevant structures using chemical intuition. We have also implemented a clustering program that selects molecules that differ from each other from a large pool of molecules to form the initial population. Through testing of EA runs with and without clustering and intuitive population creation, the following success rates were obtained; no intuition and no clustering, 28±3%, no intuition with clustering, 31±4%, with fixed intuition but without clustering, 49±5%, with fixed intuition and clustering, 49±4%, with variable intuition and without clustering, 47±4%, and with variable intuition and clustering, 50±3%. A significant increase in success rate was found when implementing intuitive population creation while clustering the initial population seems to marginally help as the population becomes more diverse.