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1,237 results for “ACS”
Dataset of publication DOI:10.1021/acsomega.7b01354 (ACS Omega)
<p>This is the underlying data of the publication: "Favorable Biological Responses of Neural Cells and Tissue Interacting with Graphene Oxide Microfibers" (DOI: 10.1021/acsomega.7b01354).</p>
INTEGRAL SPI-ACS observation of GBM-190816
<p>INTEGRAL SPI-ACS observation of GBM-190816</p>
GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank - Part AC
<p>GCTB sparse shrunk LD matrices from 2.8M common variants from the UK Biobank.</p> <p>Part <strong>AC </strong>of AA, AB, AC, AD and AE</p>
text-fig. 39. Theropod pelves in left lateral view, illustrating several pelvic characters, a, Herrerasaurus ischigualastensis; redrawn (reversed) from Novas (1993). B, Syntarsus rhodesiensis; based on QG 1, QG 691, and Raath (1990). c, Allosaurus fragilis; modified from Molnar et al. (1990). D, generalized tyrannosaurid; based on Osborn (1916) and ROM 807. E, Segnosaurus galbinensis', redrawn from Barsbold and Maryanska (1990). F, Rahonavis ostromi', based on UA 8656. Abbreviations: ac, acetabulum; bs, brevis shelf; il, ilium; ir, iliac ridge; is, ischium; of, obturator foramen; op, obturator process; pf, pubic fenestra; pip, posterior ischial process; pu, pubis. Scale bars represent 50 mm (a-b), 100 mm (c-e), and 10 mm (f). in The interrelationships and evolution of basal theropod dinosaurs
text-fig. 39. Theropod pelves in left lateral view, illustrating several pelvic characters, a, Herrerasaurus ischigualastensis; redrawn (reversed) from Novas (1993). B, Syntarsus rhodesiensis; based on QG 1, QG 691, and Raath (1990). c, Allosaurus fragilis; modified from Molnar et al. (1990). D, generalized tyrannosaurid; based on Osborn (1916) and ROM 807. E, Segnosaurus galbinensis', redrawn from Barsbold and Maryanska (1990). F, Rahonavis ostromi', based on UA 8656. Abbreviations: ac, acetabulum; bs, brevis shelf; il, ilium; ir, iliac ridge; is, ischium; of, obturator foramen; op, obturator process; pf, pubic fenestra; pip, posterior ischial process; pu, pubis. Scale bars represent 50 mm (a-b), 100 mm (c-e), and 10 mm (f).
uct c-AC-02 44um D38mm
<p># Summary:</p> <p>.X-Ray micro-computed tomography (micro-CT) of an Austin Chalk (AC) sample (a plug with 38 mm diameter), including both raw projection data and the final reconstruction, for resolution with voxel size of 44 μm.</p> <p>.The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.</p> <p>.For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p># Tomo Details:</p> <p>.Aquisition date: 2023-05-12</p> <p>.Voxel size: 44.0615 μm;</p> <p>.Camera Binning: 2;</p> <p>.Sample-source distance: 194 mm;</p> <p>.Sample-detector distance: 110 mm;</p> <p>.Optical magnification: 0.4X;</p> <p>.Filter: HE#4;</p> <p>.Beam energy: 160 kV;</p> <p>.Power: 10 W;</p> <p>.Current: 63 uA;</p> <p>.Exposure time: 5.0 sec;</p> <p>.Projections: 1601p;</p> <p># Contents:</p> <p>.uct_c-AC-02_44um_16bits.zip</p> <p>.uct_c-AC-02_44um_1601p.txrm</p> <p>.uct_c-AC-02_44um_1601p_Drift.txrm</p>
uct c-AC-01 19um D38mm
<p># Summary:</p> <p>.X-Ray micro-computed tomography (micro-CT) of an Austin Chalk (AC) sample (a plug with 38 mm diameter), including both raw projection data and the final reconstruction, for resolution with voxel size of 19 μm.</p> <p>.The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.</p> <p>.For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p># Tomo Details:</p> <p>.Aquisition date: 2023-05-12</p> <p>.Voxel size: 19.0231 μm;</p> <p>.Camera Binning: 1;</p> <p>.Sample-source distance: 135 mm;</p> <p>.Sample-detector distance: 110 mm;</p> <p>.Optical magnification: 0.4X;</p> <p>.Filter: HE#4;</p> <p>.Beam energy: 160 kV;</p> <p>.Power: 10 W;</p> <p>.Current: 63 uA;</p> <p>.Exposure time: 15.0 sec;</p> <p>.Projections: 1601;</p> <p># Contents:</p> <p>.uct_c-AC-01_19um_16bits.zip</p> <p>.uct_c-AC-01_19um_1601p.txrm</p> <p>.uct_c-AC-01_19um_1601p_Drift.txrm</p>
[Supplementary Data] PowerModel-AI: A First On-the-fly Machine-Learning Predictor for AC Power Flow Solutions.
<h1><strong>Abstract</strong></h1> <p>The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to autonomously build models while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce <em>PowerModel-AI</em>, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures the load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model's performance is sub-optimal, based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using <em>PowerModel.jl</em>, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.</p> <div> <h1><strong>Overview</strong></h1> <p>This dataset, provided as supplementary material for the above-referenced study, includes a comprehensive collection of images (plots) from the study’s analyses, along with Jupyter notebooks containing Python scripts used for the training, validation, and testing phases of PowerModel-AI. Additionally, it includes all training and external test data used in this work, generated via LANL-based open-source power flow solver, PowerModels.jl.</p> <p>The primary objective of this dataset is to ensure full reproducibility of the study’s analyses and facilitate critical examination by the scientific community, thereby maximizing the overall impact of the work.</p> <h1>Directory Structure</h1> <p>The hierarchy of folders and file organization of the dataset is illustrated in the chart below. The directory contains a README.md file which contains the information provided here and a requirements.txt that contains all libraries necessary to run the python scripts or Jupyter notebooks in this directory. A brief description of the folders and what they contain are provided below: </p> </div> <div> <strong>.</strong></div> <div>├── <strong>Models/</strong></div> <div>│ ├── _PM_AI_Models/</div> <div>│ │ ├── Type1/</div> <div>│ │ ├── Type2/</div> <div>│ │ └── Type3/</div> <div>│ │</div> <div>│ ├── _PM_AI_Module/</div> <div>│ │ ├── __init__.py</div> <div>│ │ └── PM_Methods.py</div> <div>│ │</div> <div>│ ├── _PM_JL_Data/</div> <div>│ ├── C1_Model/</div> <div>│ ├── C2_Model/</div> <div>│ ├── C3_Model/</div> <div>│ ├── M1_Model/</div> <div>│ ├── M2_Model/ </div> <div>│ └── M3_Model/</div> <div>│</div> <div>├── <strong>NodeSensitivityAnalysis/</strong></div> <div>│ ├── Sensitivity_Plots/</div> <div>│ ├── Sensitivity_PM_JL_Data/</div> <div>│ ├── get_sensitivity_PMJL_data.py</div> <div>│ └── NodeSensitivityAnalysis.ipynb</div> <div>│</div> <div>├── <strong>PlotsForOnTheFlyAnalysis/</strong></div> <div>│ ├── BaseModel_A/</div> <div>│ ├── BaseModel_B/ </div> <div>│ └── BaseModel_C/</div> <div>│ </div> <div>├── <strong>README.md</strong></div> <div>└── <strong>requirements.txt</strong></div> <p> </p> <h2>Files Description </h2> <h3><strong>1. </strong><strong>Models/</strong></h3> <p><strong><em>_PM_AI_Models/</em></strong> contains the ML models discussed in the manuscript. Type1, Type2, and Type3 refers to the C and M models with numbers "1", "2," and "3". Each Type folder contains individual subfolders for the synthetic grids discussed.</p> <p><strong> <em>_PM_AI_Module/</em></strong> contains a python script that has all the functions used in model training and analysis. It is imported in the Jupyter notebooks in the C and M subfolders in this directory.</p> <p><strong><em>_PM_JL_Data/</em> </strong>contains the following subfolders:</p> <p>a) <strong> </strong><em>_</em><strong><em><strong>G</strong>eneratePowerModelData/</em></strong> has a python script (<u>get_PowerModelJLData.py)</u> that is used to parse <u>PowerModels.jl</u> to compute AC power flow solutions for different power demand configurations. It also contains a subfolder, <strong><em>BusData_MATLAB/</em></strong>, that has all the synthetic grids used in the study and in MATLAB format.</p> <p>b) It also contains other subfolders (not shown in the chart above) that contains AC power flow solutions generated using LANL’s PowerModels.jl, for each synthetic grids and other grid-related data.</p> <p>The folders starting with C and M are the control and candidate models (more details in manuscript). Each folder contains Jupyter notebooks (for each power grid) that has python algorithms used in model training and testing, as well as functions to analyze and plot the prediction performance of the models. It accesses (if already trained) or stores (if newly trained) the models in the <strong><em>_PM_AI_Models/</em></strong> directory. Additionally, they contain 2 subfolders (not shown in the chart above): <strong>AbsoluteErrorPlots/</strong> and <strong>PredictionPlots/</strong> were the results (plots) from the analyses are stored. Some of these results are shown in the manuscript (<em>Figures 4</em>,<em> 5</em>, <em>6</em>, and <em>7</em>).</p> <h3><strong>2. </strong><strong>NodeSensitivityAnalysis/</strong></h3> <p>This folder contains the tools used for the node dependency analysis in Section 3.1.1 of the manuscript.</p> <p>It contains a python script called get_sensitivity_PMJL_data.py, which has similar operation like the <u>get_PowerModelJLData.py</u> script but only compute changes for one bus at a time (see details in manuscript). There is also a Jupyter notebook called <u>NodeSensitivityAnalysis.ipynb </u>that analyzes the bus node dependencies and produces the plots that are shown in Figure 3 in the manuscript. It contains 2 additional sub-folders:</p> <p>a) <strong> <em>Sensitivity_PM_JL_Data/</em></strong> where PowerModels.jl generated AC power flow solution data are stored, and</p> <p>b) <em><strong>Sensitivity_Plots/</strong> </em>where the results from <u>NodeSensitivityAnalysis.ipynb</u> are stored.</p> <h3><strong>3. </strong><strong>PlotsForOnTheFlyAnalysis/</strong></h3> <p>This directory contains only the results for the discussion in Section 3.2 in the manuscript, which is the on-the-fly implementation of PowerModel-AI. The on-the-fly algorithm will be provided and distributed separately in the PowerModel-AI package, which will be publicly available through LANL’s <a href="https://github.com/lanl-ansi" target="_blank" rel="noopener">The Advanced Network Science Initiative</a> (Github). It contains 3 subfolders with similar names but ends with "A", "B," and "C" which correspond to the designations shown and discussed in <em>Figure 8</em> of the manuscript.</p> <h1>Summary</h1> <h3><strong> </strong><strong>Models/</strong></h3> <p><strong>1. _PM_AI_Models/: </strong></p> <p>Contains the machine learning models discussed in the manuscript. The Type1, Type2, and Type3 folders refer to C and M models labeled "1", "2," and "3". Each type folder includes subfolders for the corresponding synthetic grids analyzed.</p> <div><strong>2. _PM_AI_Module/: </strong> </div> <div>Contains Python scripts for model training and analysis. PM_Methods.py is the script that defines functions for model training and analysis, which are imported in the Jupyter notebooks in the C and M subfolders. </div> <div> </div> <div><strong>3. _PM_JL_Data/: </strong></div> <div>-_GeneratePowerModelData/: Contains a Python script (get_PowerModelJLData.py) used to parse PowerModels.jl and compute AC power flow solutions for various power demand configurations. This folder also includes BusData_MATLAB/, which holds the synthetic grid data in MATLAB format. </div> <div>- Other Subfolders: Contain PowerModels.jl AC power flow solutions for each synthetic grid, as well as related data.</div> <div> </div> <div><strong>4. C1_Model/, C2_Model/, C3_Model/, M1_Model/, M2_Model/, M3_Model/: </strong></div> <div>These folders contain the control and candidate models (refer to manuscript details). Each folder includes pre-run Jupyter notebooks for model training and analysis, as well as two subfolders: </div> <div> - <em>AbsoluteErrorPlots/</em>: Contains saved analysis results for each grid.</div> <div> <strong> </strong>- <em>PredictionPlots/</em>: Stores model prediction results. </div> <div>Some results are shown in <em>Figures 4, 5, 6,</em> and <em>7</em> of the manuscript and can be reproduced using the notebooks.</div> <h3>NodeSensitivityAnalysis/</h3> <div><strong>1. Node Dependency Analysis Tools: </strong> </div> <div> This folder contains scripts and data used for the node dependency analysis in Section 3.1.1 of the manuscript.</div> <div> </div> <div><strong>2. Scripts: </strong></div> <div> - get_sensitivity_PMJL_data.py: Computes power flow changes for individual buses (refer to the manuscript for details). </div> <div> - NodeSensitivityAnalysis.ipynb: Analyzes dependencies and generates plots for Figure 3 in the manuscript. </div> <div> </div> <div><strong>3. Subfolders:</strong> </div> <div> - Sensitivity_PM_JL_Data/: Stores PowerModels.jl data for sensitivity analysis. </div> <div> - Sensitivity_Plots/: Contains the generated results from the Jupyter notebook.</div> <h3>PlotsForOnTheFlyAnalysis/</h3> <div><strong>On-the-Fly Learning Results: </strong>Contains the results for the on-the-fly learning analysis discussed in Section 3.2 of the manuscript. The subfolders BaseModel_A/, BaseModel_B/, and BaseModel_C/ correspond to the designations in <em>Figure 8</em> of the manuscript. These subfolders contain plots generated during analysis.</div> <h3>Additional Files</h3> <div>- README.md: This file.</div> <div>- requirements.txt: Contains a list of necessary Python libraries required to run the scripts and Jupyter notebooks.</div> <h3>Notes:</h3> <div>- The PowerModel-AI code is publicly available on GitHub.</div> <div>- The supplementary materials include additional Jupyter notebooks and results for each power grid analysis. </div> <div>- The generated results and predictions in this repository are consistent with those discussed in the manuscript.</div>
Supplementary material 4 from: Cuthbert RN, Bartlett AC, Turbelin AJ, Haubrock PJ, Diagne C, Pattison Z, Courchamp F, Catford JA (2021) Economic costs of biological invasions in the United Kingdom. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 299-328. https://doi.org/10.3897/neobiota.67.59743
Total costs of species with individual cost entries, alongside first record years and introduction pathways
Supplementary material 3 from: Cuthbert RN, Bartlett AC, Turbelin AJ, Haubrock PJ, Diagne C, Pattison Z, Courchamp F, Catford JA (2021) Economic costs of biological invasions in the United Kingdom. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 299-328. https://doi.org/10.3897/neobiota.67.59743
Web of Science search terms for UK invasive species publication numbers, alongside resulting study numbers
Supplementary material 2 from: Cuthbert RN, Bartlett AC, Turbelin AJ, Haubrock PJ, Diagne C, Pattison Z, Courchamp F, Catford JA (2021) Economic costs of biological invasions in the United Kingdom. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 299-328. https://doi.org/10.3897/neobiota.67.59743
Summary of the content of the descriptive columns of the database used in this study (adapted from Diagne et al. 2020a)
Supplementary material 1 from: Cuthbert RN, Bartlett AC, Turbelin AJ, Haubrock PJ, Diagne C, Pattison Z, Courchamp F, Catford JA (2021) Economic costs of biological invasions in the United Kingdom. In: Zenni RD, McDermott S, García-Berthou E, Essl F (Eds) The economic costs of biological invasions around the world. NeoBiota 67: 299-328. https://doi.org/10.3897/neobiota.67.59743
Subset of InvaCost database used for analyses of UK invasion costs. Note that cost data are not annualised.
Supplementary material 1 from: Dimitriou AC, Taiti S, Schmalfuss H, Sfenthourakis S (2018) A molecular phylogeny of Porcellionidae (Isopoda, Oniscidea) reveals inconsistencies with present taxonomy. In: Hornung E, Taiti S, Szlavecz K (Eds) Isopods in a Changing World. ZooKeys 801: 163-176. https://doi.org/10.3897/zookeys.801.23566
Percentage sequence divergence among the main clades of Porcellionidae and Maximum Likelihood phylogenetic tree. :
Dataset for the publication "Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications"
<p>This is dataset for paper published:</p> <p>Letizia, P.S.; Signorino, D.; Crotti, G. Impact of DC Transient Disturbances on Harmonic Performance of Voltage Transformers for AC Railway Applications. <em>Sensors</em> <strong>2022</strong>, <em>22</em>, 2270. https://doi.org/10.3390/s22062270</p>
Supplementary material 1 from: Neagu AC, Manolache S, Rozylowicz L (2022) The drums of war are beating louder: Media coverage of brown bears in Romania. Nature Conservation 50: 65-84. https://doi.org/10.3897/natureconservation.50.86019
Appendix S1. Coding categories (adapted from Hughes et al. 2020) and descriptive statistics of analyzed media articles
Dataset for the paper "Oxygen reduction reaction activity in non-precious single atom (M-N/C) catalysts – contribution of metal and carbon/nitrogen framework-based sites", 2023, ACS Catalysis, DOI:10.1021/acscatal.3c00356
<p>The data in this spreadsheet was used to produce the figures in the paper</p> <p>Authors:Mengjun Gong, Asad Mehmood, Basit Ali, Kyung-Wan Nam and Anthony Kucernak</p> <p>Title:Oxygen reduction reaction activity in non-precious single atom (M-N/C) catalysts – contribution of metal and carbon/nitrogen framework-based sites</p> <p>Journal:ACS Catalysis</p> <p>DOI:10.1021/acscatal.3c00356</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.7879881</p>
Code and Data for "AC Josephson effect in a gate-tunable Cd3As2 nanowire superconducting weak link"
<p>This data set contains Python code to evaluate Shapiro maps and the measurement data, its metadata as well as figures used for the publication "AC Josephson effect in a gate-tunable Cd<sub>3</sub>As<sub>2</sub> nanowire superconducting weak link".</p>
CMR in the Assessment of Patient With ACS in the Emergency Room
ClinicalTrials.gov study NCT00564382. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Harmonizing Optimal Strategy for Treatment of Coronary Artery Diseases Trial - Comparison of REDUCTION of PrasugrEl Dose & POLYmer TECHnology in ACS Patients (HOST REDUCE POLYTECH RCT Trial)
ClinicalTrials.gov study NCT02193971. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Impact of Residual Syntax Score and Syntax Revascularization Index on Outcomes of ACS Patients With Multi-Vessel Disease
ClinicalTrials.gov study NCT03138473. IPD Sharing: NO. Countries: 1. Publications: 19.
Optimum Troponin Cutoffs for ACS in the ED
ClinicalTrials.gov study NCT01994577. IPD Sharing: Not stated. Countries: 1. Publications: 7.
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Allen Brain Atlas
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.