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75 results for “Control parameters”
Investigating the correlation of control parameters in reversed wood biomass supply chains (NCN) DEC-2020/39/I/HS4/03533
<p>Results of calculations of mutual correlations between parameters of reversed wood biomass supply chains, referring to the statistical yearbooks of the State Forests (in Poland). Identification of predictions of increasing values of individual parameters of reversed wood biomass supply chains.<br>The study was financed by National Science Centre in Poland under agreement DEC-2020/39/I/HS4/03533</p>
NHM model control and parameter files
<p>Model files used in NHM operational model</p> <p>2024/10/10 Updated structure that includes forecasting. Updated restart file, and adjusted myparam.param file.</p>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
Data for Floor heating pre-on/off parameters based on Model Predictive Control feature extrapolation Paper in CLIMA2022 conference proceedings
<p>This is a collection of time series results used to obtain all the results shown in the paper. The tags of the .csv or .mat files are self explanatory and easy to use.</p>
Control Treatment/No Fertilizer - Shoot Growth Parameters (mm) and Post-harvest Fresh and Dry Biomasses (g) Amaranthus tricolor – Agronomy-Basel MDPI 2022.
<p>1. Raw datasets for the measurements of the shoot growth parameters (mm) of Amaranthus tricolor; heights (mm), leaf lengths (mm), leaf widths (mm), shoot widths (mm), stem widths (mm), root lengths (mm), root widths (mm), and number of leaves. </p> <p>2. Raw datasets for the measurements of the post-harvest fresh and dry biomasses (g) of Amaranthus tricolor; total fresh weights (g), total dry weights (g), shoot fresh weights (g), shoot dry weights (g), stem fresh weights (g), stem dry weights (g), leaf fresh weights (g), leaf dry weights (g), root fresh weights (g), root dry weights (g). </p>
Multi-physic analysis of power electronic control parameters in a simulation framework
<p>Dataset for conference paper "Multi-physic analysis of power electronic control parameters in a simulation framework"</p>
Adaptive Parameter Control for Search-Based Unit Test Generation — Replication Package
<h1>Running the experiments</h1> <p>Prerequisites for running the experiments:</p> <ul> <li><a href="https://www.docker.com/" target="_blank" rel="noopener">Docker</a></li> <li><a href="https://python-poetry.org/" target="_blank" rel="noopener">Poetry</a></li> </ul> <p>Steps to run the experiments:</p> <ol> <li>Download the experiment zip file you wish to run (<code>single_parameter_experiment.zip</code> or <code>multi_parameter_experiment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd single_parameter_experiment</code>).</li> <li>Run <code>poetry install --only main</code> to install all dependencies.</li> <li>To run the experiment, run <code>poetry run python run_experiment.py</code>.</li> <li>All results can be found in the folder <code>data/</code>.</li> </ol> <p>The modules used for the experiment are defined in the file <code>experiment_modules.py</code> and to see the experiment configuration, look in <code>run_experiment.py</code>.</p> <p><strong>Warning</strong>: the experiments take several weeks to run on a single machine, therefore it is advisable to split the experiments based on modules and run them in parallel.</p> <h1>Running the analysis</h1> <p>Prerequisites for running the analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the analysis:</p> <ol> <li>Download the analysis zip file (<code>analysis-adaptive-parameter-control.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd analysis-adaptive-parameter-control</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>If you want to re-run the Bayesian models locally on your machine, follow the <strong>optional</strong> step below, otherwise download and unzip the trace data from the replication package, i.e., <code>Trace data single.zip</code> and <code>Trace data multi.zip</code>. </li> <li>Place the <code>.nc</code> files in the corresponding folder: <code>analysis-adaptive-parameter-control/single_parameter/</code> or <code>analysis-adaptive-parameter-control/multi_parameter/</code>. <ol> <li>E.g. the <code>coverage_rate_model_single_parameter.nc</code> goes in the <code>single_parameter</code> folder, while the <code>coverage_rate_model_multi_parameter.nc</code> goes in the <code>multi_parameter</code> folder.</li> </ol> </li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Data analysis" and run all cells in order.</li> </ol> <h3>(Optional) Running the Bayesian models locally before the analysis.</h3> <ol> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open a notebook of choice (<code>coverage_rate_multi_parameter.ipynb</code>, <code>coverage_rate_single_parameter.ipynb</code>, <code>final_coverage_multi_parameter.ipynb</code>, <code>final_coverage_single_parameter.ipynb</code>, <code>overhead_model_multi_parameter.ipynb</code>, or <code>overhead_model_single_parameter.ipynb</code>).</li> <li>Navigate to the section called "Model specification" and run the three notebook cells.</li> </ol> <p><strong>Warning</strong>: this will take a long time, if you don't have the time, use the following alternative instead</p> <h1>Data</h1> <p>The data from when we ran the experiments is available in the <code>Single data.zip</code> and <code>Multi data.zip</code> files.</p> <p>The structure of these are the following:</p> <ul> <li>There are folders for each module the experiment was run on, further divided into each unique run. All these folders include: <ul> <li>Coverage reports.</li> <li>Complete logs for the unique run.</li> <li>A timeline over controlled parameter values during the test generation process.</li> <li>The complete Pynguin configuration for the run.</li> <li>The generated test suite.</li> </ul> </li> <li>There is one <code>statistics.csv</code> file containing some information about each run and their branch coverage timelines.</li> </ul> <h1>Running the parameter assignment analysis</h1> <p>Prerequisites for running the parameter assignment analysis:</p> <ul> <li><a href="https://conda.io/projects/conda/en/latest/user-guide/getting-started.html" target="_blank" rel="noopener">Conda</a></li> </ul> <p>Steps to run the parameter assignment analysis:</p> <ol> <li>Download the analysis zip file (<code>parameter-assignment.zip</code>).</li> <li>Un-zip the file.</li> <li>Open a terminal and navigate to the unzipped folder (e.g. <code>cd parameter-assignment</code>).</li> <li>Run the following command to install the conda environment and all dependencies: <code>conda env create -f environment.yml</code></li> <li>Navigate to the notebooks folder (<code>Notebooks/</code>).</li> <li>Open the notebook <code>parameter_assignment_analysis.ipynb</code>.</li> <li>Run all cells in order.</li> </ol>
Annealing rate as a crucial parameter controlling the photoelectrochemical properties of AuCu mosaic core-shell nanoparticles
<p>The following dataset contains research data that is the basis of the research article:</p> <p>"Annealing rate as a crucial parameter controlling the photoelectrochemical properties of AuCu mosaic core-shell nanoparticles"</p> <p>Contents of the package are the following:</p> <p>a) Linear voltammetry carried out in 0.1 M NaOH for 1F-AuCuTiND, 1F-TiND, 1S-AuCuTiND, 1S-TiND, 4F-AuCuTiND, 4S-AuCuTiND</p> <p>b) Electrochemical impedance spectroscopy measurement for 1F-AuCuTiND, 1F-TiND, 1S-AuCuTiND, 1S-TiND</p> <p>c) Experimental results of UV-vis absorbance for 1F-AuCuTiND, 1S-AuCuTiND, 4F-AuCuTiND, 4S-AuCuTiND</p>
Ability of the ash dieback pathogen to reproduce and to induce damage on its host are controlled by different environmental parameters
<p>Ash dieback, induced by an invasive ascomycete, <em>Hymenoscyphus</em> <em>fraxineus</em>, has emerged in the late 1990s as a severe disease threatening ash populations in Europe. Future prospects for ash are improved by the existence of individuals with natural genetic resistance or tolerance to the disease and by limited disease impact in many environmental conditions where ash is common. Nevertheless, it was suggested that, even in those conditions, ash trees are infected and enable pathogen transmission. We studied the influence of climate and local environment on the ability of <em>H. fraxineus</em> to infect, be transmitted and cause damage on its host. We showed that healthy carriers, i.e. individuals showing no dieback but carrying <em>H. fraxineus</em>, exist and may play a significant role in ash dieback epidemiology. The environment strongly influenced <em>H. fraxineus</em> with different parameters being important depending on the life cycle stage. The ability of <em>H. fraxineus</em> to establish on ash leaves and to reproduce on the leaf debris in the litter (rachises) mainly depended on total precipitation in July-August and was not influenced by local tree cover. By contrast, damage to the host, and in particular shoot mortality was significantly reduced by high summer temperature in July-August and by high autumn average temperature. As a consequence, in many situations, ash trees are infected and enable H. fraxineus transmission while showing limited or even no damage. We also observed a decreasing trend of severity (leaf necrosis and shoot mortality probability) with the time of disease presence in a plot that could be significant for the future of ash dieback.</p>
Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
<p>Data for Oyster reefs' control of carbonate chemistry - implications for oyster reef restoration in estuaries subject to coastal ocean acidification. </p>
The Effect of Intranasal Vasoconstrictor Medications on Hemodynamic Parameters: A Randomized Double-blind, Placebo-controlled Trial.
ClinicalTrials.gov study NCT02285634. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Monitoring data for oyster reefs and nearby controls, including high frequency environmental parameters, carbonate chemistry, oyster growth, and other data
Open the record for dataset details and reuse information.
Ability of the ash dieback pathogen to reproduce and to induce damage on its host are controlled by different environmental parameters
Open the record for dataset details and reuse information.
Computerized analysis of brain MRI parameters dynamics in young patients with Cushing Syndrome – a case-control study
Open the record for dataset details and reuse information.
Data from: Palaeobiogeographical structuration of Smithian (Early Triassic) ammonoid faunas within the western USA basin and its controlling parameters
We present the first quantitative palaeobiogeographical analysis in terms of distribution and abundance of Early Triassic ammonoids from the western USA basin during the Smithian, c. 1 myr after the Permian–Triassic boundary mass extinction. The faunal dataset consists of a taxonomically homogenized compilation of spatial and temporal occurrences and abundances from 27 sections distributed within the western USA basin. Two complementary multivariate techniques were applied to identify the main biogeographical structuring recorded in the analysed presence/absence data: additive cluster analysis using the neighbor‐joining algorithm (NJ) and non‐metric multidimensional scaling (NMDS). Regarding abundance data, a taxonomic diversity (sensu evenness) analysis was coupled with graphical comparisons of relative abundances of selected taxa. The identified relationships indicate that middle Smithian ammonoids of the western USA basin were geographically organized in terms of both distribution and abundance, with the biogeographical distinction of a southern and a northern cluster. This N–S structuring in the distribution and abundance of middle Smithian ammonoids is notably paralleled by the relative amount of siliciclastics, which suggests that clastic load of the water column was a major controlling factor. In marked contrast with the middle Smithian, the studied late Smithian ammonoid assemblages do not show any significant differences, whatever the depositional environment. This abrupt biogeographical homogenization, independent from intrabasinal facies heterogeneity, indicates a switch from regional to global drivers, associated with the well‐known late Smithian global extinction and remarkable cosmopolitan ammonoid distributions during that time.
Flexible Automation and Intelligent Manufacturing 2024, Taiwan - Optimization of Thermal Induction Welding Parameters in LPBFM for Enhanced Adhesion Control
<p>Laser powder bed fusion of metals (LPBFM) is an additive manufacturing process in which powdered alloy is selectively fused together to form near net shape parts. A method for qualifying a model to optimize thermal conduction welding parameters to omit lack of fusion defects is proposed. The approach takes into account: material, geometry and previously experimentally derived, robust process parameters.</p> <p><br>It entails carrying out specific experiments to generate data for calibration and validation of the model for fine-tuning of process parameters to compensate for the changing boundary conditions throughout the build. The objective is to achieve defined adhesion criteria for the meltpools of each laser scan vector.</p> <p><br>The method transforms the build job preparation of LPBFM into an optimization problem by determining the required precision and defining requirements for the model approach to be applicable on the shopfloor. The work contributes to a model-based quality assurance of LPBFM technology.</p>
NHM UC model control and parameter files
<p>PRMS data file for UC operational test case</p>
Rate-and-state friction parameters for "Frictional Characteristics of Oceanic Transform Faults: Progressive Deformation and Alteration Controls Seismic Style"
<p>Rate-and-state friction parameters to accompany "Frictional Characteristics of Oceanic Transform Faults: Progressive Deformation and Alteration Controls Seismic Style"</p>
Supplementary Dataset for the Paper: "Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Re-Training"
<p>Supplementary dataset for paper Hose, Henrik, Alexander Gräfe, and Sebastian Trimpe. "Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Re-Training." <em>arXiv preprint arXiv:2404.05835</em> (2024).</p> <p>The code to use this dataset is publicly available at <a href="https://github.com/hshose/Adaptive-AMPC-Cartpole">https://github.com/hshose/Adaptive-AMPC-Cartpole</a></p> <p>The dataset contains training and testing data to train an NN controller for a standart cartpole system.</p> <p>For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. Additionally, sensitivities (i.e. gradients dU/dtheta with respect to some system parameters theta) are provided in a file called `J.txt`.</p>
Soil parameters measured in European Mole (Talpa europaea) mounds and nearby control areas on a meadow near Galgahévíz, Hungary
<p>Soil parameters measured by the near-infrared device of Agrocares Ltd (the Netherlands): pH(H2O), soil organic matter (%), P (M3) (mg/kg), total nitrogen (g/kg), exchangeable K, Mg and Ca (mmol/kg), organic carbon (g/kg), potentially mineralizable nitrogen (g/kg), cation exchange capacity (mmol/kg), total Al (g/kg), total Fe (g/kg), clay (%) and soil moisture (%) </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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.