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- Creator:
- Arthurs, Christopher J., Khlebnikov, Rostislav, Melville, Alexander, Marčan, Marija, Gomez, Alberto, Dillon-Murphy, Desmond, Cuomo, Federica, Vieira, Miguel, Schollenberger, Jonas, Lynch, Sabrina, Tossas-Betancourt, Christopher, Iyer, Kritika, Hopper, Sara, Livingston, Elizabeth, Youssefi, Pouya, Noorani, Alia, Ben Ahmed, Sabrina, Nauta, Foeke J.N., van Bakel, Theodorus M.J., Ahmed, Yunus, van Bakel, Petrus A.J., Mynard, Jonathan, Di Achille, Paolo, Gharahi, Hamid, Lau, Kevin D., Filonova, Vasilina, Aguirre, Miquel, Nama, Nitesh, Xiao, Nan, Baek, Seungik, Garikipati, Krishna, Sahni, Onkar, Nordsletten, David, and Figueroa, Carlos A.
- Description:
- This repository contains the source code for the CRIMSON GUI, as required in the PLOS Computational Biology publication: CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation by the same authors., This is a snapshot of the software; build dependencies can be found at https://doi.org/10.7302/ssj9-n788. Please visit https://github.com/carthurs/CRIMSONGUI/releases/tag/PLOS_Comp_Bio & www.crimson.software for more general information and the most up to date version of the software., and Software can be compiled in Windows.
- Keyword:
- Blood Flow Simulation, Patient-specific, Open-source Software, Image-based simulation, Cardiovascular Medical Image, Segmentation, and Finite Element Simulation
- Citation to related publication:
- CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation C.J. Arthurs, R. Khlebnikov, A. Melville, M. Marčan, A. Gomez, D. Dillon-Murphy, F. Cuomo, M.S. Vieira, J. Schollenberger, S.R. Lynch, C. Tossas-Betancourt, K. Iyer, S. Hopper, E. Livingston, P. Youssefi, A. Noorani, S. Ben Ahmed, F.J.H. Nauta, T.M.J. van Bakel, Y. Ahmed, P.A.J. van Bakel, J. Mynard, P. Di Achille, H. Gharahi, K. D. Lau, V. Filonova, M. Aguirre, N. Nama, N. Xiao, S. Baek, K. Garikipati, O. Sahni, D. Nordsletten, C.A. Figueroa bioRxiv 2020.10.14.339960; doi: https://doi.org/10.1101/2020.10.14.339960 and Computational Vascular Biomechanics Lab @ the University of Michigan and other collaborators, The Qt Company, NSIS Team and contributors, PostgreSQL Global Development Group, Oracle Corporation, Kitware. CRIMSON open source project - Build Dependencies [Data set], (2021). University of Michigan - Deep Blue. https://doi.org/10.7302/ssj9-n788
- Discipline:
- Health Sciences and Engineering
-
- Creator:
- Nason, Samuel R., Mender, Matthew J., Vaskov, Alex K., Willsey, Matthew S., Ganesh Kumar, N., Kung, Theodore A., Patil, Parag G., and Chestek, Cynthia A.
- Description:
- This data is a subset of the data used to generate components of all figures in the manuscript and supplement in Nason et al., 2021, Neuron. The purpose of the study was to demonstrate the first-ever simultaneous brain-control of two independent groups of fingers in one hand with some analysis of cortical tuning to finger movements in nonhuman primates. This advises future brain-machine interfaces for the control of finger movements with humans. All of the data is contained in .mat files, which can be commonly opened by Matlab and the Python scipy library. The Matlab packages (and versions) used for the manuscript are: MATLAB (9.4), Signal Processing Toolbox (8.0), Statistics and Machine Learning Toolbox (11.3), and Curve Fitting Toolbox (3.5.7).
- Keyword:
- Brain-machine interface, Prosthesis, and Upper extremity
- Citation to related publication:
- Nason, S.R., Mender, M.J., Vaskov, A.K., Willsey, M.S., Ganesh Kumar, N., Kung, T.A., Patil, P.G., and Chestek, C.A. (2021). Real-Time Linear Prediction of Simultaneous and Independent Movements of Two Finger Groups Using an Intracortical Brain-Machine Interface. Neuron (accepted).
- Discipline:
- Engineering
-
- Creator:
- Nason, Samuel R., Vaskov, Alex K., Willsey, Matthew S., Welle, Elissa J., An, Hyochan, Vu, Philip P., Bullard, Autumn J., Nu, Chrono S., Kao, Jonathan C., Shenoy, Krishna V., Jang, Taekwang, Kim, Hun-Seok, Blaauw, David, Patil, Parag G., and Chestek, Cynthia A.
- Description:
- This data is a subset of the data used to generate figures similar to figures 1, 2, 3, and 4 in Nason et al., 2020, Nature Biomedical Engineering. The purpose of the study was to demonstrate the benefits of using spiking band power, a low-power but single unit specific recording signal, for brain-machine interfaces with nonhuman primates with the potential to impact low-power brain-machine interfaces with humans. All of the data is contained in .mat files, which can be commonly opened by Matlab and the Python scipy library.
- Keyword:
- Brain-machine interface, Prosthesis, and Neural recording
- Citation to related publication:
- Nason, S.R., Vaskov, A.K., Willsey, M.S., Welle, E.J., An, H., Vu, P.P., Bullard, A.J., Nu, C.S., Kao, J.C., Shenoy, K.V., Jang, T., Kim, H.-S., Blaauw, D., Patil, P.G., and Chestek, C.A. (2020). A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat. Biomed. Eng. 4, 973–983. https://doi.org/10.1038/s41551-020-0591-0
- Discipline:
- Engineering
-
- Creator:
- Agnit Mukhopadhyay
- Description:
- Conducting quantitative metrics-based performance analysis of first-principles-based global magnetosphere models is an essential step in understanding their capabilities and limitations, and providing scope for improvements in order to enhance their space weather prediction capabilities for a range of solar conditions. In this study, a detailed comparison of the performance of three global magnetohydrodynamic (MHD) models in predicting the Earth’s magnetopause location and ionospheric cross polar cap potential (CPCP) has been presented. Using the Community Coordinated Modeling Center’s Run-on-Request system and extensive database on results from various magnetospheric scenarios simulated for a variety of solar wind conditions, the aforementioned model predictions have been compared for magnetopause standoff distance estimations obtained from six empirical models, and with cross polar cap potential estimations obtained from the Assimmilative Mapping of Ionospheric Electrodynamics (AMIE) Model and the Super Dual Auroral Radar Network (SuperDARN) observations. We have considered a range of events spanning different space weather activity to analyze the performance of these models. Using a fit performance metric analysis for each event, we have quantified the models’ reproducibility of magnetopause standoff distances and CPCP against empirically-predicted observations, and identified salient features that govern the performance characteristics of the modeled magnetospheric and ionospheric quantities.
- Citation to related publication:
- Mukhopadhyay, A., Jia, X., Welling, D. T., & Liemohn, M. W. (2021). Global Magnetohydrodynamic Simulations: Performance Quantification of Magnetopause Distances and Convection Potential Predictions. Frontiers in Astronomy and Space Sciences, 8. https://doi.org/10.3389/fspas.2021.637197
- Discipline:
- Engineering and Science
-
- Creator:
- Arthurs, Christopher J., Khlebnikov, Rostislav, Melville, Alexander, Marčan, Marija, Gomez, Alberto, Dillon-Murphy, Desmond, Cuomo, Federica, Vieira, Miguel, Schollenberger, Jonas, Lynch, Sabrina, Tossas-Betancourt, Christopher, Iyer, Kritika, Hopper, Sara, Livingston, Elizabeth, Youssefi, Pouya, Noorani, Alia, Ben Ahmed, Sabrina, Nauta, Foeke J.N., van Bakel, Theodorus M.J., Ahmed, Yunus, van Bakel, Petrus A.J., Mynard, Jonathan, Di Achille, Paolo, Gharahi, Hamid, Lau, Kevin D., Filonova, Vasilina, Aguirre, Miquel, Nama, Nitesh, Xiao, Nan, Baek, Seungik, Garikipati, Krishna, Sahni, Onkar, Nordsletten, David, and Figueroa, Carlos A.
- Description:
- This repository contains the source code for the CRIMSON Flow Solver as required in the PLOS Computational Biology publication: CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation by the same authors., This is a snapshot of the software. Please visit https://github.com/carthurs/CRIMSONFlowsolver/releases/tag/PLOS_Comp_Bio & www.crimson.software for more general information and the most up to date version of the software. , and Software can be compiled in Cygwin and Linux.
- Keyword:
- Blood Flow Simulation, Patient-specific, Open-source Software, Image-based simulation, Cardiovascular Medical Image, Segmentation, and Finite Element Simulation
- Citation to related publication:
- CRIMSON: An Open-Source Software Framework for Cardiovascular Integrated Modelling and Simulation C.J. Arthurs, R. Khlebnikov, A. Melville, M. Marčan, A. Gomez, D. Dillon-Murphy, F. Cuomo, M.S. Vieira, J. Schollenberger, S.R. Lynch, C. Tossas-Betancourt, K. Iyer, S. Hopper, E. Livingston, P. Youssefi, A. Noorani, S. Ben Ahmed, F.J.H. Nauta, T.M.J. van Bakel, Y. Ahmed, P.A.J. van Bakel, J. Mynard, P. Di Achille, H. Gharahi, K. D. Lau, V. Filonova, M. Aguirre, N. Nama, N. Xiao, S. Baek, K. Garikipati, O. Sahni, D. Nordsletten, C.A. Figueroa bioRxiv 2020.10.14.339960; doi: https://doi.org/10.1101/2020.10.14.339960 and Arthurs, C., Khlebnikov, R., Melville, A., Marčan, M., Gomez, A., Dillon-Murphy, D., Cuomo, F., Vieira, M., Schollenberger, J., Lynch, S., Tossas-Betancourt, C., Iyer, K., Hopper, S., Livingston, E., Youssefi, P., Noorani, A., Ben Ahmed, S., Nauta, F., van Bakel, T., Ahmed, Y., van Bakel, P., Mynard, J., Di Achille, P., Gharahi, H., Lau, K., Filonova, V., Aguirre, M., Nama, N., Xiao, N., Baek, S., Garikipati, K., Sahni, O., Nordsletten, D., Figueroa, C. (2021). CRIMSON open source project - Graphical User Interface (GUI) Source Code for PLOS Computational Biology [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/679b-dw96
- Discipline:
- Engineering and Health Sciences
-
- Creator:
- Mukhopadhyay, Agnit, Daniel T Welling, Michael W Liemohn, Aaron J Ridley, Shibaji Chakrabarty, and Brian J Anderson
- Description:
- An updated auroral conductance module is built for global models, using nonlinear regression & empirical adjustments to span extreme events., Expanded dataset raises the ceiling of conductance values, impacting the ionospheric potential dB/dt & dB predictions during extreme events., and Application of the expanded model with empirical adjustments refines the conductance pattern, and improves dB/dt predictions significantly.
- Keyword:
- Space Weather Forecasting, Extreme Weather, Ionosphere, Magnetosphere, MI Coupling, Ionospheric Conductance, Auroral Conductance, Aurora, SWMF, SWPC, Nonlinear Regression, and dB/dt
- Citation to related publication:
- Mukhopadhyay, A., Welling, D. T., Liemohn, M. W., Ridley, A. J., Chakraborty, S., & Anderson, B. J. (2020). Conductance Model for Extreme Events: Impact of Auroral Conductance on Space Weather Forecasts. Space Weather, 18(11), e2020SW002551. https://doi.org/10.1029/2020SW002551
- Discipline:
- Engineering and Science
-
- Creator:
- Ahluwalia, Vinayak S., Steimle, Lauren N., and Denton, Brian T.
- Description:
- This repository includes test instances of infinite-horizon Markov decision processes with multiple models of parameters (i.e., "Multi-model Markov decision processes"). We generated each test instance in the dataset using a Python script. The test instances can be read in using the provided C++ and Python script. See the README for details.
- Keyword:
- Markov decision processes, mixed-integer programming, stochastic programming, and dynamic programming
- Citation to related publication:
- Ahluwalia, Steimle, and Denton. "Policy-based branch-and-bound for infinite-horizon Multi-model Markov decision processes". 2020.
- Discipline:
- Engineering
-
- Creator:
- Swiger, Brian M., Liemohn, Michael W., and Ganushkina, Natalia Y.
- Description:
- We sampled the near-Earth plasma sheet using data from the NASA Time History of Events and Macroscale Interactions During Substorms mission. For the observations of the plasma sheet, we used corresponding interplanetary observations using the OMNI database. We used these data to develop a data-driven model that predicts plasma sheet electron flux from upstream solar wind variations. The model output data are included in this work, along with code for analyzing the model performance and producing figures used in the related publication. and Data files are included in hdf5 and Python pickle binary formats; scripts included are set up for use of Python 3 to access and process the pickle binary format data.
- Keyword:
- neural network, plasma sheet, solar wind, machine learning, keV electron flux, deep learning, and space weather
- Citation to related publication:
- Swiger, B. M., Liemohn, M. W., & Ganushkina, N. Y. (2020). Improvement of Plasma Sheet Neural Network Accuracy With Inclusion of Physical Information. Frontiers in Astronomy and Space Sciences, 7. https://doi.org/10.3389/fspas.2020.00042
- Discipline:
- Science and Engineering
-
- Creator:
- Eckels, Joshua D.
- Description:
- The goal of the research was to train a surrogate model for the prediction of electric field distribution for a given electrospray emitter geometry design. The surrogate is to be used in reduced-fidelity modeling of electrospray thruster arrays. The code repository is included in the README.txt file.
- Keyword:
- Electrospray design, Martinez-Sanchez hyperboloid solution, and Electrospray engineering toolkit (ESPET)
- Citation to related publication:
- J.D. Eckels, C.B. Whittaker, B.A. Jorns, A.A. Gorodetsky, B. St. Peter, R.A. Dressler, “Simulation-based surrogate methodology of electric field for electrospray emitter geometry design and uncertainty quantification”, presented at the 37th International Electric Propulsion Conference, Boston, MA USA, June19-23, 2022 Available: https://www.electricrocket.org/IEPC_2022_Papers.html
- Discipline:
- Engineering
-
- Creator:
- Ponder, Brandon M., Ridley, Aaron J., Goel, Ankit, and Bernstein, Dennis S.
- Description:
- This research was completed to statistically validate that a data-model refinement technique could integrate real measurements to remove bias from physics-based models via changing the forcing parameters such as the thermal conductivity coefficients.
- Keyword:
- Thermosphere, GITM, CHAMP, GRACE, MSIS, Upper Atmosphere Modeling, and Data Assimilation
- Citation to related publication:
- Ponder, B. M., Ridley, A. J., Goel, A., & Bernstein, D. S. (2023). Improving forecasting ability of GITM using data-driven model refinement. Space Weather, 21, e2022SW003290. https://doi.org/10.1029/2022SW003290
- Discipline:
- Engineering and Science