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- Creator:
- Shah, Bhavarth
- Description:
- The three approaches used three distinct datasets named as follows: Historicalwater_levels.csv, Historical_Precipitation.csv, and Bayesian Statistical dataset.csv. These files are accessible using Microsoft Office or similar software. The machine learning models are developed in Jupyter Notebook (.ipynb) files, named according to the datasets they utilize. However, for the third approach, the models are named Random Forest, LSTM Model Base, and Multivariate LSTM Models. More details are available on the Shah_Bhavarth_Readme.txt. These notebooks can be accessed through Python, Project Jupyter, or Google Colab, and dependencies include libraries such as Pandas, NumPy, Matplotlib, Scikit-learn, Keras, and TensorFlow. The supplementary material also includes Excel files for stage-curve calculations and diversions, named Water_levels_Stage_Curve_Calculations1970-2018.xlsx and Diversions_calculation.xlsx, respectively.
- Keyword:
- Machine learning, Forecasting, Water levels, Mono lake, and Hydrology
- Citation to related publication:
- Shah, Bhavarth. 2024. "Mono Lake Water Levels Forecasting Using Machine Learning." Master’s thesis, University of Michigan, School for Environment and Sustainability. ORCID iD: 0000-0002-2391-8610. https://dx.doi.org/10.7302/22659
- Discipline:
- Science and Engineering
-
- Creator:
- Hong, Yi, Fry, Lauren M., Orendorf, Sophie, Ward, Jamie L., Mroczka, Bryan, Wright, David, and Gronewold, Andrew
- Description:
- Accurate estimation of hydro-meteorological variables is essential for adaptive water management in the North American Laurentian Great Lakes. However, only a limited number of monthly datasets are available nowadays that encompass all components of net basin supply (NBS), such as over-lake precipitation (P), evaporation (E), and total runoff (R). To address this gap, we developed a daily hydro-meteorological dataset covering an extended period from 1979 to 2022 for each of the Great Lakes. The daily P and E were derived from six global gridded reanalysis climate datasets (GGRCD) that include both P and E estimates, and R was calculated from National Water Model (NWM) simulations. Ensemble mean values of the difference between P and E (P – E) and NBS were obtained by analyzing daily P, E, and R. Monthly averaged values derived from our new daily dataset were validated against existing monthly datasets. This daily hydro-meteorological dataset has the potential to serve as a validation resource for current data and analysis of individual NBS components. Additionally, it could offer a comprehensive depiction of weather and hydrological processes in the Great Lakes region, including the ability to record extreme events, facilitate enhanced seasonal analysis, and support hydrologic model development and calibration. The source code and data representation/analysis figures are also made available in the data repository.
- Keyword:
- Great Lakes, Hydrometeorological, National Water Model, Daily, Overlake precipitation, Overlake evaporation, Total runoff, Net Basin Supply, and Water Balance
- Discipline:
- Science and Engineering
-
- Creator:
- Klinich, Kathleen D, Hu, Jingwen, Boyle, Kyle J, Manary, Miriam A., and Orton, Nichole R
- Description:
- As part of a project to develop side impact test procedures for evaluating wheelchairs, wheelchair tiedowns and occupant restraint systems (WTORS), and vehicle-based occupant protection systems for wheelchair seating stations, we created validated finite element (FE) models to support procedure development. Models were constructed using LS-DYNA. Dynamic sled tests were performed to validate the FE models of surrogate fixtures and commercial hardware. Validated FE models were developed for the Surrogate wheelchair base (SWCB), Surrogate wheelchair for side impact (SWCSI), a manual wheelchair (Ki Mobility Catalyst 5), and a power wheelchair (Quantum Rehab Edge 2.0). Additional FE models of a heavy-duty anchor meeting the Universal Docking Interface Geometry (UDIG), surrogate four-point strap tiedowns (SWTORS), a traditional docking station, and the surrogate wall fixture were also developed.
- Keyword:
- finite element, wheelchair, transportation, and tiedown
- Discipline:
- Engineering
-
- Creator:
- Lee, Shih Kuang, Tsai, Sun Ting, and Glotzer, Sharon C.
- Description:
- The trajectory data and codes were generated for our work "Classification of complex local environments in systems of particle shapes through shape-symmetry encoded data augmentation" (amidst peer review process). The data sets contain trajectory data in GSD file format for 7 test systems, including cubic structures, two-dimensional and three-dimensional patchy particle shape systems, hexagonal bipyramids with two aspect ratios, and truncated shapes with two degrees of truncation. Besides, the corresponding Python code and Jupyter notebook used to perform data augmentation, MLP classifier training, and MLP classifier testing are included.
- Keyword:
- Machine Learning, Colloids Self-Assembly, Crystallization, and Order Parameter
- Citation to related publication:
- https://doi.org/10.48550/arXiv.2312.11822
- Discipline:
- Other, Science, and Engineering
-
- Creator:
- Luyet, Chloe, Elvati, Paolo, Vinh, Jordan, and Violi, Angela
- Description:
- A growing body of work has linked key biological activities to the mechanical properties of cellular membranes, and as a means of identification. Here, we present a computational approach to simulate and compare the vibrational spectra in the low-THz region for mammalian and bacterial membranes, investigating the effect of membrane asymmetry and composition, as well as the conserved frequencies of a specific cell. We find that asymmetry does not impact the vibrational spectra, and the impact of sterols depends on the mobility of the components of the membrane. We demonstrate that vibrational spectra can be used to distinguish between membranes and, therefore, could be used in identification of different organisms. The method presented, here, can be immediately extended to other biological structures (e.g., amyloid fibers, polysaccharides, and protein-ligand structures) in order to fingerprint and understand vibrations of numerous biologically-relevant nanoscale structures.
- Keyword:
- molecular dynamics, membranes, mechanical vibration, bacterial identification, and Staphylococcus aureus
- Citation to related publication:
- Luyet C, Elvati P, Vinh J, Violi A. Low-THz Vibrations of Biological Membranes. Membranes. 2023; 13(2):139. https://doi.org/10.3390/membranes13020139
- Discipline:
- Engineering
-
- Creator:
- Elvati, Paolo, Luyet, Chloe, Wang, Yichun, Liu, Changjiang, VanEpps, J. Scott, Kotov, Nicholas A., and Violi, Angela
- Description:
- Amyloid nanofibers are abundant in microorganisms and are integral components of many biofilms, serving various purposes, from virulent to structural. Nonetheless, the precise characterization of bacterial amyloid nanofibers has been elusive, with incomplete and contradicting results. The present work focuses on the molecular details and characteristics of PSMa1-derived functional amyloids present in Staphylococcus aureus biofilms, using a combination of computational and experimental techniques, to develop a model that can aid the design of compounds to control amyloid formation. Results from molecular dynamics simulations, guided and supported by spectroscopy and microscopy, show that PSMa1 amyloid nanofibers present a helical structure formed by two protofilaments, have an average diameter of about 12 nm, and adopt a left-handed helicity with a periodicity of approximately 72 nm. The chirality of the self-assembled nanofibers, an intrinsic geometric property of its constituent peptides, is central to determining the fibers' lateral growth.
- Keyword:
- molecular self-assembly, computational nanotechnology, nanobiotechnology, and structural properties
- Citation to related publication:
- Paolo Elvati, Chloe Luyet, Yichun Wang, Changjiang Liu, J. Scott VanEpps, Nicholas A. Kotov, and Angela Violi ACS Applied Nano Materials 2023 6 (8), 6594-6604 DOI: 10.1021/acsanm.3c00174
- Discipline:
- Engineering and Science
-
- Creator:
- Wallace, Dylan M, Benyamini, Miri, Nason-Tomaszewski, Samuel R, Costello, Joseph T, Cubillos, Luis H, Mender, Matthew J, Temmar, Hisham, Willsey, Matthew S, Patil, Parag P, Chestek, Cynthia A, and Zacksenhouse, Miriam
- Description:
- This is data from Wallace, Benyamini et al., 2023, Journal of Neural Engineering. There are two sets of data included: 1. Neural features and error labels used to train error classifiers for each day used in the study 2. Trial data from an example experiment day (Monkey N, Day 6), with runs for offline calibration, online brain control, error monitoring, and error correction. The purpose of this study was to investigate the use of error signals in motor cortex to improve brain-machine interface (BMI) performance for control of two finger groups. All data is contained in .mat files, which can be opened using MATLAB or the Python SciPy library.
- Keyword:
- Brain-machine interface (BMI), Error detection, and Neural recording
- Citation to related publication:
- Wallace, D. M., Benyamini, M., Nason-Tomaszewski, S. R., Costello, J. T., Cubillos, L. H., Mender, M. J., Temmar, H., Willsey, M. S., Patil, P. G., Chestek, C. A., & Zacksenhouse, M. (2023). Error detection and correction in intracortical brain–machine interfaces controlling two finger groups. Journal of Neural Engineering, 20(4), 046037. https://doi.org/10.1088/1741-2552/acef95
- Discipline:
- Engineering, Science, and Health Sciences
-
- Creator:
- Sun, Hu, Ren, Jiaen, Chen, Yang, Zou, Shasha, Chang, Yurui, Wang, Zihan, and Coster, Anthea
- Description:
- Our research focuses on providing a fully-imputed map of the worldwide total electron content with high resolution and spatial-temporal smoothness. We fill in the missing values of the original Madrigal TEC maps via estimating the latent feature of each latitude and local time along the 2-D grid and give initial guess of the missing regions based on pre-computed spherical harmonics map. The resulting TEC map has high imputation accuracy and the ease of reproducing. All data are in HDF5 format and are easy to read using the h5py package in Python. The TEC map is grouped in folders based on years and each file contains a single-day data of 5-min cadence. Each individual TEC map is of size 181*361. and WARNING: 2023-12-01 the data file for 2019-Jan-03 has badly fitted values. Please avoid using it. All other days' files are ready to use.
- Keyword:
- Total Electron Content, Matrix Completion, VISTA, Spherical Harmonics, and Spatial-Temporal Smoothing
- Citation to related publication:
- Sun, H., Hua, Z., Ren, J., Zou, S., Sun, Y., & Chen, Y. (2022). Matrix completion methods for the total electron content video reconstruction. The Annals of Applied Statistics, 16(3), 1333-1358., Sun, H., Chen, Y., Zou, S., Ren, J., Chang, Y., Wang, Z., & Coster, A. (2023). Complete Global Total Electron Content Map Dataset based on a Video Imputation Algorithm VISTA. Scientific Data, in press., and Zou, S., Ren, J., Wang, Z., Sun, H., & Chen, Y. (2021). Impact of storm-enhanced density (SED) on ion upflow fluxes during geomagnetic storm. Frontiers in Astronomy and Space Sciences, 8, 746429.
- Discipline:
- Science and Engineering
-
- Creator:
- Klinich, Kathleen D, Lin, Brian, and Moore, Jamie L.
- Description:
- This dataset allows comparison of the different strategies implemented by vehicle manufacturers being used to communicate with drivers. Spreadsheets were created in MS Excel to summarize data for each vehicle, and include page numbers in each vehicle owner's manual for reference. The photos taken of each vehicle control panel allow detailed inspection of the displays and controls.
- Keyword:
- vehicle, controls, displays, and FMVSS 101
- Discipline:
- Engineering
-
- Creator:
- Brian, Chen
- Description:
- The procedure followed while creating this data is summarized in Section II of Chen, Brian, et al. "Behavioral cloning in atari games using a combined variational autoencoder and predictor model." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021. This data is not a result of a research but an intermediate product that is used in research. This dataset is generated to train a behavioral cloning framework from gameplay screen captures and keystrokes of an "expert" player. The RL agent that is trained using "RL Baselines Zoo package" acts as the "expert" player, whose decision making process we desire to learn. In addition to behavioral cloning experiments, this dataset is further used to demonstrate the efficacy of a novel incremental tensor decomposition algorithm on image-based data streams.
- Keyword:
- Imitation Learning, Behavioral Cloning, Reinforcement Learning, Machine Learning, and Gameplay Data
- Citation to related publication:
- Chen, Brian, et al. "Behavioral cloning in atari games using a combined variational autoencoder and predictor model." 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2021., Aksoy, Doruk, et al. "An Incremental Tensor Train Decomposition Algorithm." arXiv preprint arXiv:2211.12487 (2022)., and Chen, Brian, et al. "Low-Rank Tensor-Network Encodings for Video-to-Action Behavioral Cloning", forthcoming
- Discipline:
- Engineering and Science