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- Creator:
- Chen, Yuxi
- Description:
- We use the MHD with embedded particle-in-cell model (MHD-EPIC) to study the Geospace Environment Modeling (GEM) dayside kinetic processes challenge event at 01:50-03:00 UT on 2015-11-18, when the magnetosphere was driven by a steady southward IMF. In the MHD-EPIC simulation, the dayside magnetopause is covered by a PIC code so that the dayside reconnection is properly handled. We compare the magnetic fields and the plasma profiles of the magnetopause crossing with the MMS3 spacecraft observations. Most variables match the observations well in the magnetosphere, in the magnetosheath, and also during the current sheet crossing. The MHD-EPIC simulation produces flux ropes, and we demonstrate that some magnetic field and plasma features observed by the MMS3 spacecraft can be reproduced by a flux rope crossing event. We use an algorithm to automatically identify the reconnection sites from the simulation results. It turns out that there are usually multiple X-lines at the magnetopause. By tracing the locations of the X-lines, we find the typical moving speed of the X-line endpoints is about 70~km/s, which is higher than but still comparable with the ground-based observations.
- Keyword:
- MHD, PIC, and simulation
- Citation to related publication:
- Chen, Y., Tóth, G., Hietala, H., Vines, S. K., Zou, Y., Nishimura, Y., Silveira, M. V. D., Guo, Z., Lin, Y., & Markidis, S. (2020). Magnetohydrodynamic With Embedded Particle-In-Cell Simulation of the Geospace Environment Modeling Dayside Kinetic Processes Challenge Event. Earth and Space Science, 7(11), e2020EA001331. https://doi.org/10.1029/2020EA001331 and Chen, Yuxi, et al. "Magnetohydrodynamic with embedded particle-in-cell simulation of the Geospace Environment Modeling dayside kinetic processes challenge event." arXiv preprint arXiv:2001.04563 (2020). https://arxiv.org/abs/2001.04563
- Discipline:
- Science
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- Creator:
- Sugrue, Dennis P.
- Description:
- This data was collected and processed as part of ongoing research to characterize waterway infrastructure performance in the Great Lakes. These dataset enable researchers to evaluate both travel time and vessel carrying capacity in the waterway., I assembled AIS data from the MarineCadastre website for UTM Zones 15-18 for the years 2015-2017 available in csv format. I combined files for Navigation Seasons, defined as March to January and clipped data for a set of predefined features using a python code (AIS Data Processor.ipynb). The code writes the appended and clipped files to csv for a single Navigation Year. The written files are submitted here: Trimmed_NY2015_new.csv (n=13,228,824); Trimmed_NY2016_new.csv (n=18,782,779); Trimmed_NY2017_new.csv (n=16,816,603), Data fusion of AIS and LPMS used the following algorithm for a subset of 30 vessels on the waterway. Let A be the original AIS data and let B be the subset of records for vessel i within geographic feature j. The script for this analysis is attached (Maritime Data Fusion.ipynb), For Connecting Channels and select segments of the Great Lakes: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic feature, Gj. Let B_ij⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as vessel i arrival to feature j, b_ijt | 4. IF feature j is a harbor or lock, select tmax for each unique date or any consecutive dates, record as departure from feature j, b_ijt | 5. Calculate time elapsed between features for each vessel, For vessel passage through the Soo Locks: 1. Subset A for vessel i. Let B_i⊆A | 2. Subset B_i in geographic boundaries (46.5<Lat<46.6, -84.4<Lon<-84.3). Let C_(i,lock)⊆B_i | 3. Select tmin for each unique date or any consecutive dates, record as arrival to Soo Locks | 4. Select tmax for each unique date or any consecutive dates, record as departure to Soo Locks | 5. Calculate time delta between arrival and departure times, and The merged dataset is included here along with the raw LPMS data: Merged_Data_new.csv (n=42,021), LPMS obscured.csv (n=55,342). VesselNames have been obscured in these datasets to protect proprietary information for shipping companies.
- Keyword:
- Maritime Transportation Efficiency, Data Fusion, Waterway Performance
- Citation to related publication:
- Sugrue, D., Adriaens, P. (in review) Multi-dimensional Data Fusion to Evaluate Waterway Performance: Maritime Transport Efficiency of Iron Ore on the Great Lakes. Water Resources Research.
- Discipline:
- Engineering
-
- Creator:
- Agrawal, Mayank and Glotzer, Sharon C
- Description:
- Micron-scale robots require systems that can morph into arbitrary target configurations controlled by external agents such as heat, light, electricity, and chemical environment. Achieving this behavior using conventional approaches is challenging because the available materials at these scales are not programmable like their macroscopic counterparts. To overcome this challenge, we propose a design strategy to make a robotic machine that is both programmable and compatible with colloidal-scale physics. Our strategy uses motors in the form of active colloidal particles that constantly propel forward. We sequence these motors end-to-end in a closed chain forming a two-dimensional loop that folds under its mechanical constraints. We encode the target loop shape and its motion by regulating six design parameters, each scale-invariant and achievable at the colloidal scale. The research dataset includes simulation, visualization, and analysis scripts and results generated for the 2D chain loops of self-propelling particles. File Description:, -- arrows_folding - Contains the data for the folded chain loop shapes resembling an arrowhead., -- bending_vs_variation - Contains the data to study the stability of a particular shape in simulations as one of the segments of the shape bends and/or the distribution of propulsion on it varies., -- curved_triangle - Contains the data to study motion and bending of a triangle shape made using chain loop., -- example_shapes - Contains data for various examples of shapes that can be generated by designing the chain loops., -- nskT_vs_fakT - Contains the data for a specific shape to study the effect of scaling up the number of particles (governed by ns) and the propulsion (governed by fa) in its chain., -- stability - Contains the data and theoretical model (stability.py) to study the stability of the six different shapes., -- tuning_design_forM - Contains the data for sequential tuning the design parameters to fold the shape "M" as described in the corresponding publication., and -- two_neighboring_cds_segments_ - Contains the data to study a system of two neighboring chain segments with respect to different parameters discussed in the publication.
- Keyword:
- active particles, colloidal robotics, design, kilobots, and morphological control
- Citation to related publication:
- Agrawal, M, Glotzer SC. (2020). Scale-free, programmable design of morphable chain loops of kilobots and colloidal motors. PNAS. www.pnas.org/cgi/doi/10.1073/pnas.1922635117
- Discipline:
- Engineering
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- Creator:
- Arbic, Brian K. and Luecke, Conrad A.
- Description:
- The locations ("locs") files in this directory contain indices pointing to the locations in the CMA superset datafiles that were used in the Luecke et al. 2020 comparison of HYCOM and MITgcm model output to CMA observations.
- Keyword:
- Physical oceanography, Numerical modeling, Ocean modeling, Model/data comparisons, and Internal gravity waves
- Citation to related publication:
- Luecke, C. A., Arbic, B. K., Richman, J. G., Shriver, J. F., Alford, M. H., Ansong, J. K., Bassette, S. L., Buijsman, M. C., Menemenlis, D., Scott, R. B., Timko, P. G., Voet, G., Wallcraft, A. J., & Zamudio, L. (2020). Statistical Comparisons of Temperature Variance and Kinetic Energy in Global Ocean Models and Observations: Results From Mesoscale to Internal Wave Frequencies. Journal of Geophysical Research: Oceans, 125(5), e2019JC015306. https://doi.org/10.1029/2019JC015306
- Discipline:
- Science
-
- Creator:
- Moser, Carol, Schoenebeck, Sarita , and Resnick, Paul
- Description:
- These data, survey instruments (including informed consent) and analysis scripts come from Carol Moser's dissertation titled, Impulse Buying: Designing for Self-Control with E-commerce.
- Keyword:
- Impulse Buying, Self-control, and Experimental Design
- Discipline:
- Social Sciences
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- Creator:
- Moniri, Saman, Xiao, Xianghui, and Shahani, Ashwin J.
- Description:
- The data is comprised of 22 directories, each housing a .hdf file of the X-ray projections recorded during solidification of Al-Si-Cu-Sr. The flat and dark projections are also included as two separate .hdf files (total file count: 24). The raw data file is in .hdf format and can be reconstructed into .tiff, e.g., by using the TomoPy toolbox in Python.
- Keyword:
- Crystallization, growth modifiers, silicon, in situ, X-ray tomography
- Citation to related publication:
- Wang, Y., Gao, J., Ren, Y., De Andrade, V., & Shahani, A. J. (2020). Formation of a Three-Phase Spiral Structure Due to Competitive Growth of a Peritectic Phase with a Metastable Eutectic. JOM, 72(8), 2965–2973. https://doi.org/10.1007/s11837-020-04237-x
- Discipline:
- Engineering
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- Creator:
- Liemohn, Michael W
- Description:
- The editorial decision process for the Journal of Geophysics Research Space Physics is assisted by over 1,000 scientists every year, providing over 3,000 reviews per year. These statistics are presented for the years 2013 through 2018, showing some fluctuations but, overall, consistency in the response of the space physics research community to requests to serve as manuscript reviewers. Over half of these reviews are submitted on time, and the average time to review actually dropped as the load increased. This is greatly appreciated and the community is to be commended and thanked for their willingness to help make this journal thrive and remain a premiere publication in the field.
- Keyword:
- Editorial and reviewer statistics
- Citation to related publication:
- Liemohn, M. W. (2020). Editorial: Multiyear analysis of JGR Space Physics reviewing statistics. Journal of Geophysical Research Space Physics, 125, e2019JA027719. https://doi.org/10.1029/2019JA027719
- Discipline:
- Science
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- Creator:
- Whitaker, Steven T., Nataraj, Gopal, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A.
- Description:
- File: P,jf06Sep2019,mese.7 The multi-echo spin echo (MESE) data was acquired using a 3D acquisition with an initial 90 degree excitation pulse followed by 32 refocusing (180 degree) pulses, resulting in 32 echoes with echo spacing of 10 ms. The repetition time of the sequence was 1200 ms. Each refocusing pulse was flanked by crusher gradients to impart 14 cycles of phase across the imaging volume. The initial excitation pulse had time-bandwidth product of 6, duration of 3 ms, and slab thickness of 0.9 cm, and each refocusing pulse had time-bandwidth product of 2, duration of 2 ms, and slab thickness of 2.1 cm. The scan took 36 min 11 s and covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: P,jf06Sep2019,b1.7 The Bloch-Siegert (BS) scans were acquired using a 3D acquisition. The excitation pulse of these scans had time-bandwidth product of 8 and duration of 1 ms. The pair of scans used +/-4 kHz off-resonant Fermi pulses between excitation and readout. The BS scans took 2 min 40 s in total and covered a FOV of 22 x 22 x 0.99 cm^3 with matrix size 200 x 50 x 9., File: P,jf06Sep2019,mwf.7 The small-tip fast recovery (STFR) scans were acquired using a 3D acquisition. The first two and last two scans were pairs of spoiled gradient-recalled echo (SPGR) scans with echo time difference of 2.3 ms. (In the related paper, only the first set was used, i.e., only 11 of the 13 scans were used.) The remaining scans used scan parameters that were optimized to minimize the Cramer-Rao Lower Bound (CRLB) of estimates of myelin water fraction (MWF). The RF pulses had time-bandwidth product of 8 and duration of 1 ms. Each pair of SPGR scans took 58 s and the nine STFR scans took 3 min 36 s for a total scan time of 5 min 32 s (4 min 34 s if one pair of SPGR scans is ignored). The scans covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: meseslice5.mat Contains the 32 echoes of the MESE image data for the middle slice of the imaging volume. Saved using Mathworks MATLAB R2019a., File: b1slice5.mat Contains the transmit field inhomogeneity map for the middle slice of the imaging volume., File: recon.jld Key "img" contains the 11 STFR images for the middle slice of the imaging volume. Key "b0map" contains a field map estimated from the two SPGR scans. Key "mask" contains a mask of the voxels for which to estimate MWF. Key "T1img" contains a T1-weighted image for anatomical reference., File: headmask.mat Contains a mask for visualizing just the brain (ignores the skull) for the middle slice of the imaging volume., File: rois.mat Contains masks for various regions of interest (ROIs), used for computing statistics. Keys "mtopleft", "mtopright", "mbottomleft", and "mbottomright" refer to the corresponding locations on the anatomical reference image (see related paper). Key "mic" refers to the internal capsules, and key "mgm" refers to a gray matter ROI., The raw data files (P-files) can be read into the Julia programming language using the Julia version of the Michigan Image Reconstruction Toolbox ( https://github.com/JeffFessler/MIRT.jl) or into MATLAB using TOPPE ( https://github.com/toppeMRI/toppe). The reconstructed slices used in the related paper are provided for convenience, and are stored in .mat files that can be loaded into Julia (using the package MAT.jl) or MATLAB, and a .jld file that can be loaded into Julia (using the package JLD.jl). The Julia code for processing the data to create MWF maps is hosted publicly on GitHub at https://github.com/StevenWhitaker/STFR-MWF., and Files: toppe-master.zip and MIRT.jl-master.zip are archived versions of the TOPPE and Michigan Image Reconstruction Toolbox code sets from GitHub as of 2/28/2020.
- Keyword:
- myelin, machine learning, kernel learning, magnetic resonance imaging, and scan design
- Citation to related publication:
- Whitaker, S. T., Nataraj, G., Nielsen, J.-F., & Fessler, J. A. (2020). Myelin water fraction estimation using small-tip fast recovery MRI. Magnetic Resonance in Medicine, 84(4), 1977–1990. https://doi.org/10.1002/mrm.28259
- Discipline:
- Health Sciences and Engineering
-
- 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
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- Creator:
- Hatch, Nan E.
- Description:
- Crouzon FGFR2-C342Y/+ and wild type littermate pups on a C57BL/6 congenic background were injected with lentivirus expressing recombinant TNAP enzyme or phosphate buffered saline shortly after birth. Mice were euthanized 3 weeks after birth for analyses.
- Keyword:
- craniofacial, bone, craniosynotosis, FGFR2, TNAP tissue nonspecific alkaline phosphatase, mouse model, and development
- Citation to related publication:
- Nam, H. K., Vesela, I., Schutte, S. D., & Hatch, N. E. (2020). Viral delivery of tissue nonspecific alkaline phosphatase diminishes craniosynostosis in one of two FGFR2C342Y/+ mouse models of Crouzon syndrome. PLOS ONE, 15(5), e0234073. https://doi.org/10.1371/journal.pone.0234073
- Discipline:
- Health Sciences