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27 results for “STIR”
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model
<p>Annotation of inverted repeats displaying features of pble STIR or IR in the hg38 genome model. The annotation of <em>pble</em>-like inner inverted repeats was done using Palindrome (EMBOSS package). The output file was then filtered using pal2gff (https://github.com/Leelouh/pal2gff/blob/main/pal2gff.py), using as parameters a repeat size between 5 and 15 nucleotides, a spacer between pairs of inverted repeats (IRs) of 2 to 10 nucleotides, and a number of mismatches within repeats ranging from 0 to 1. These parameters were chosen taking into account those of the inner IRs found at ends of invertebrate pbles.</p>
STIR ROOT Consistency Test Data
<p>Data generated by `ROOT_STIR_consistency` for the `test_view_offset_root` test. This data is pre-generated GATE data of point sources, measured by a GATE geometry (similar to the GE Discovery 690), that is to be used to test the allignment between STIR and GATE crystal positions.</p>
Syngas kinetics inside steady Perfectly Stirred Reactor
<p>We present the dataset and python scripts used in our autoencoder (AE) neural network (NN)-based reduced chemistry work (<a href="https://www.dl.begellhouse.com/journals/558048804a15188a,22e553d25a1b5ff0,65e7b0537eb64be1.html">Zhang and Sankaran, 2022</a>).</p> <p>1. To train the AE NN, run "<strong>python Train_PSR_AE_PCA.py</strong> " with Keras </p> <p>2. The dataset is about syngas combustion inside 0-D steady perfectly stirred reactor (PSR) at a wide range of parameter conditions.</p> <ol> <li>The fuel is CO, H2, N2 with a volume ratio 5:1:4. The oxidizer is O2 and N2 mixed in 1:3 by volume. The inflow temperature is 500 K and combustion occurs at atmospheric pressure.</li> <li>In total, there are 1.63 million samples with equivalence ratio varying from 0.09 to 20.0 and residence time scale varying to cover the entire S-curve.</li> <li>The dataset is in hdf5 format and can be loaded with the python script, <strong>load_data_h5.py</strong>. Inside the dataset, there 12 entries.</li> </ol> <ul> <li>1. asciiListtmp = h5f['vars_name'][()] ##name of the 12 thermochemical state variables</li> <li>2. para_Phi = h5f['parameters_Phi'][()] ##equivalence ratio, varying from 0.09 to 20.0</li> <li>3. para_Tin = h5f['parameters_Tin'][()] ##inflow temperature, constant=500 [K]</li> <li>4. para_inv_tau_res = h5f['parameters_inv_tau_res'][()] ##inverse of residence time, varying from 4.53e-09 to 1.54e+04 [1/s]</li> <li>5. x_train_min = h5f['trainset_min'][()] ##minimum value of training set</li> <li>6. x_train_max = h5f['trainset_max'][()] ##maximum value of training set</li> <li>7. x_data = h5f['dataset'][()] ##Thermochemical state variables (temperature, mass fractions of chemical species), normalized with x_train_min and x_train_max to be [-1,1]</li> <li>8. x_src_data = h5f['dataset_src'][()] ##source term * 2/(x_train_max-x_train_min)</li> <li>9. x_del_data = h5f['dataset_del'][()] ##(xinflow-x)* 2/(x_train_max-x_train_min)</li> <li>10. train_ind = h5f['train_dataset_indices'][()] #0.7, sample index of training set </li> <li>11. test_ind = h5f['test_dataset_indices'][()] #0.3*0.5, sample index of test set</li> <li>12. vali_ind = h5f['valid_dataset_indices'][()] #0.3*0.5, sample index of validation set</li> <li>The training/test/validation splitting is used in our reduced chemistry work. </li> </ul> <p> </p>
Fig. 1. A in Scientific Note The more stirring the better: cichlid fishes associate with foraging potamotrygonid rays
Fig. 1. A freshwater ray (Potamotrygon motoro) forages with use of "undulate the disc and stir substrate" tactic. Note fine clouds of sediment adjacent to the ray.
Fig. 2. Association between a in Scientific Note The more stirring the better: cichlid fishes associate with foraging potamotrygonid rays
Fig. 2. Association between a foraging freshwater ray (Potamotrygon falkneri) and two species of cichlid fishes (Crenicichla britskii on the left and Geophagus proximus on the right). The ray settles close to the bottom, begins to undulate the disc and stir the substrate, which cause the cichlid to approach (a); as the ray proceeds foraging and forms a fine sediment cloud, the cichlids hover head-down close to the disc and watches potential prey to be uncovered by the ray's movements (b).
Fig. 1 in Scientific Note Stirring, charging, and picking: hunting tactics of potamotrygonid rays in the upper Paraná River
Fig. 1. Two hunting behaviors of potamotrygonid rays. Potamotrygon falkneri undulating its disc close to the bottom, stirring the substrate and uncovering hidden prey (a), and Potamotrygon orbignyi approaching a tree stump to pick snails adhered above water surface (not visible on the photograph) (b). The former species is from the study area mentioned in this paper, whereas the latter species was observed in the Maranhão River in Goiás State, Central Brazil.
Fig. 2 in Scientific Note Stirring, charging, and picking: hunting tactics of potamotrygonid rays in the upper Paraná River
Fig. 2. Hunting tactics of Potamotrygon falkneri and Potamotrygon motoro at the Paraná River. Settling close to the bottom, undulating the disc to stir the substrate, and engulfing uncovered prey trapped under the disc (a); approaching the shallows, charging towards concentrated preys, and engulfing prey trapped under the disc (b); approaching a submerged tree stump, ascending towards surface, and exposing anterior part of the disc to pick snails adhered slightly above water surface (c).
Scaled and Translated Image Recognition (STIR)
<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2211.10288">[2211.10288] Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks (arxiv.org)</a><br> <strong>Code:</strong> <a href="https://github.com/taltstidl/scale-equivariant-cnn">taltstidl/scale-equivariant-cnn: Official code for "Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks" (github.com)</a></p> <p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Using the dataset</strong></p> <p>Depending on which data you are planning to use, download one or more of the following files. Data is stored in compressed <code>.npz</code> format and can be loaded as documented <a href="https://numpy.org/doc/stable/reference/generated/numpy.load.html">here</a>.</p> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>emoji.npz</code></td> <td>Emoji vector icons rendered as white icon on black background</td> </tr> <tr> <td><code>mnist.npz</code></td> <td>Classic MNIST handwritten digits rescaled to varying sizes</td> </tr> <tr> <td><code>trafficsign.npz</code></td> <td>Traffic signs from street imagery downscaled to varying sizes</td> </tr> <tr> <td><code>aerial.npz</code></td> <td>Objects in aerial imagery downscaled to varying sizes</td> </tr> </tbody> </table> <p>Each file contains multiple arrays that can be accessed in a dictionary-like fashion. The keys are documented below, where <code>n</code> is the number of classes for a given file and <code>m</code> is the number of instances for each class. Both <code>emoji.npz</code> (36 classes, 1 instance) and <code>mnist.npz</code> (10 classes, 50 instances) are in black & white while <code>trafficsign.npz</code> (16 classes, 25 instances) and <code>aerial.npz</code> (9 classes, 25 instances) are in color.</p> <table> <thead> <tr> <th>Key</th> <th>Shape</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>imgs</code></td> <td><code>(3, 48, n, m, 64, 64)</code> black & white, <code>(3, 48, n, 64, 64, 3)</code> color</td> <td>Images grouped into 3 sets (training, validation, testing) and 48 different scales. Values will be in range <code>0</code> to <code>255</code>.</td> </tr> <tr> <td><code>lbls</code></td> <td><code>(3, 48, n, m)</code></td> <td>Indices referencing ground truth labels. See <code>lbldata</code> for descriptive names. Values will be in range <code>0</code> to <code>n - 1</code>.</td> </tr> <tr> <td><code>scls</code></td> <td><code>(3, 48, n, m)</code></td> <td>Known scales as given by bounding box size. Values will be in range <code>17</code> to <code>64</code>.</td> </tr> <tr> <td><code>psts</code></td> <td><code>(3, 48, n, m, 2)</code></td> <td>Known position of bounding box. First value is distance to left edge, second value distance to top edge.</td> </tr> <tr> <td><code>metadata</code></td> <td><code>(6, 2)</code></td> <td>Metadata on title, description, author, license, version and date.</td> </tr> <tr> <td><code>lbldata</code></td> <td><code>(n,)</code></td> <td>Descriptive names for each ground truth labels.</td> </tr> </tbody> </table> <p>For use in Python a dataset class is provided that implements the basic functionality for loading a certain split and scale selection, as illustrated in the code below. It ensures shuffling is done in a consistent manner such that ground truth scales and positions can be retrieved. Metadata and label descriptions can be retrieved via <code>metadata</code> and <code>labeldata</code>, respectively.</p> <pre><code class="language-python">from data.dataset import STIRDataset dataset = STIRDataset('data/emoji.npz') # Obtain images and labels for training images, labels = dataset.to_torch(split='train', scales=[32, 64], shuffle=True) # Obtain known scales and positions for above scales, positions = dataset.get_latents(split='train', scales=[32, 64], shuffle=True) # Get metadata and label descriptions metadata = dataset.metadata label_descriptions = dataset.labeldata</code></pre> <p><strong>License and Attribution</strong></p> <p>When using this dataset for your own research, please respect the individual licenses of the original data. These are distributed within the data files' metadata. For attribution in papers, we recommend the following citations.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Stir sago
<p>Stir sago dough.</p> <p>Photo taken on 15.02.2016 in Bulu, Nafra circle, West Kameng, Arunachal Pradesh, India.</p>
Chemical Process Technologies: Continuous Stirred Tank Reactor
<p>Overview of the continuously stirred tank reactor (CSTR) in chemical processing</p>
Phase 2 STIR Trial: Haploidentical Transplant and Donor Natural Killer Cells for Solid Tumors
ClinicalTrials.gov study NCT02100891. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data for Thermo-Mechanical Analysis of Friction Stir Welding
<p><span>This study explores the development and application of machine learning (ML) metamodels for the thermo-mechanical analysis of Friction Stir Welding (FSW). The main objective is to address the challenge of accurately predicting the thermo-mechanical behaviour of materials in FSW processes. Using finite element models, a high-fidelity dataset consisting of 20 Hammersley design datapoints is generated which is then used to develop a low-fidelity dataset of 420 datapoints using KNN imputation. </span></p>
Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates
<p>Python and Fiji code used for the study "Adaptive preservation of orphan ribosomal proteins in chaperone-stirred condensates"</p>
Mesh data for multi-impeller mixing performance prediction in stirred tanks using mean age theory approach
<p>The upload files include mesh data for all configurations studied in the research: multi-impeller mixing performance prediction in stirred tanks using mean age theory approach.</p>
Data from: What's stirring in the reservoir? modelling mechanisms of henipavirus circulation in fruit bat hosts
Open the record for dataset details and reuse information.
Accompanying files: Automated device for continuous stirring while sampling in liquid chromatography systems
<p>3D STL files of the components required for reproducing the stirring device, as well as supplementary files. Full details are available at the full manuscript or from the corresponding author.</p> <p><br> Acknowledgements: O.M. is funded through the NWA StartImpuls. This work has been funded by the ERC (AdG 741774), the NWO (Vici grant 724.012.002) and the Dutch Ministry of Education, Culture and Science (Gravitation program 024.001.035). We thank Andreas Hussain for discussions.</p>
Transcriptional analysis of nitrite oxidizing Nitrospira growing in a continuous stirred tank reactor
GEO Series GSE123406. Nitrospira moscoviensis. 9 samples. Type: Expression profiling by high throughput sequencing.
Preservation of orphan ribosomal proteins during stress in chaperone-stirred condensates
GEO Series GSE237174. Saccharomyces cerevisiae. 8 samples. Type: Expression profiling by high throughput sequencing.
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