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9 results for “mixed-mode”
Short Fatigue Crack Behavior under various Level of Mixed-Mode
<p>This is dataset to paper: Short Fatigue Crack Behavior under various Level of Mixed-Mode</p>
An inventory of school buildings in Central Sulawesi (Indonesia) developed through a mixed-mode data collection approach
<p>Urban disaster risk management and reduction requires the development and periodic updating of regional building inventories. However, the development of such inventories can be very cost-intensive and time-consuming, making this a challenging task, particularly for low- and middle-income countries.</p> <p>We used a combination of side-walk and remote survey methods to develop an inventory of 2536 school buildings in Central Sulawesi, Indonesia. The Central Sulawesi region was affected by the 28th September 2018 Palu earthquake. The moment magnitude (Mw) 7.5 earthquake caused intense ground shaking in the epicentral area, triggering liquefaction, tsunami and major landslides, leading to over 4,000 fatalities and 165,000 displaced people. At least 1299 schools were reported to have been damaged or collapsed, affecting about 184,000 students.</p> <p>The developed inventory includes information on location, number of stories, occupancy characteristics (building use and population), age, type of lateral load resisting systems, building materials, structural vulnerabilities, and structural conditions following the 2018 earthquake.</p> <p>The developed database can be used in pre-event/long-term risk analysis and management, post-event/near-real-time loss estimation, and regional-level decision-making on school assets and related policies. The database has been made available for public use and can be readily harmonised with similar databases for other regions.</p> <p>This data repository also contains information on interviews with 30 school principals in the region. The interview details the damage states and functional recovery trajectory of school buildings in the 30 schools.</p>
Database of mixed-mode ventilated transitional spaces
<p>Transitional spaces are meant as “<em>a space in between indoor and outdoor climate, or between two indoor environments, which thermal characteristics can be or not modified by mechanical control system and where occupant may experience the dynamic effect of this change”.</em> Nowadays, transitional spaces are pivotal in non-residential architecture and the proportion of such areas may vary between 10% up to 40% of the total volume depending on the typology. Mixed-mode buildings are desings with by both active and passive systems. </p> <p>The necessity of buildings’ energy demand and consumption reduction is a tangible fact after the entry into force of the EPBD directive 31/2010/EU. As a consequence buildings design went through the improvement of thermal envelope proprieties. In particular, for non-residential buildings this aspect combined with the increased level of internal gains has changed the energy balance: low heating demand compensated by an increased cooling demand not restricted to the only summer season.</p> <p>This evidence has stimulated a renewed interest in designers for the exploitation of natural ventilation as means of passive cooling solutions. Transitional spaces rely on a great potential for natural ventilation exploitation. They have direct connection with the outside, their architecture based on large volume suites with the principles for an effective natural ventilation.</p> <p>Because of the awareness of natural ventilation limitations, which relies on natural forces, the interest has progressively moved towards mixed-mode buildings. Mixed-mode buildings are indeed designed in such way that the HVAC system help to prevent uncomfortable conditions when natural ventilation is not sufficient to guarantee a comfortable environment.</p> <p>With the aim of a better overview about the role of transitional spaces in mixed-modeventilation design, technical information about 17 non-residential mixed-mode buildings among Europe have been recovered and analysed. All the information about transitionals spaces typology, mixed-mode scheme, ventilation strategy, controls and HVAC integration have been collected in a small database presented by an excel factsheet.</p>
Fracture toughness of mixed-mode anticracks in highly porous materials dataset and data processing
<blockquote> <div>This repository contains the code and datasets used in the data analysis for "Fracture toughness of mixed-mode anticracks in highly porous materials". The analysis is implemented in Python, using Jupyter Notebooks.</div> </blockquote> <h2>Contents</h2> <ul> <li><code>main.ipynb</code>: Jupyter notebook with the main data analysis workflow.</li> <li><code>energy.py</code>: Methods for the calculation of energy release rates.</li> <li><code>regression.py</code>: Methods for the regression analyses.</li> <li><code>visualization.py</code>: Methods for generating visualizations.</li> <li><code>df_mmft.pkl</code>: Pickled DataFrame with experimental data gathered in the present work.</li> <li><code>df_legacy.pkl</code>: Pickled DataFrame with literature data.</li> </ul> <h2>Prerequisites</h2> <ul> <li>To run the scripts and notebooks, you need:</li> <li>Python 3.12 or higher</li> <li>Jupyter Notebook or JupyterLab</li> <li>Libraries: <code>pandas</code>, <code>matplotlib</code>, <code>numpy</code>, <code>scipy</code>, <code>tqdm</code>, <code>uncertainties</code>, <code>weac</code></li> </ul> <h2>Setup</h2> <ol> <li>Download the zip file or clone this repository to your local machine.</li> <li>Ensure that Python and Jupyter are installed.</li> <li>Install required Python libraries using <code>pip install -r requirements.txt</code>.</li> </ol> <h2>Running the Analysis</h2> <ol> <li>Open the <code>main.ipynb</code> notebook in Jupyter Notebook or JupyterLab.</li> <li>Execute the cells in sequence to reproduce the analysis.</li> </ol> <h2>Data Description</h2> <div>The data included in this repository is encapsulated in two pickled DataFrame files, <code>df_mmft.pkl</code> and <code>df_legacy.pkl</code>, which contain experimental measurements and corresponding parameters. Below are the descriptions for each column in these DataFrames:</div> <h3><code>df_mmft.pkl</code></h3> <div>Includes data such as experiment identifiers, datetime, and physical measurements like slope inclination and critical cut lengths.</div> <ul> <li><code>exp_id</code>: Unique identifier for each experiment.</li> <li><code>datestring</code>: Date of the experiment as a string.</li> <li><code>datetime</code>: Timestamp of the experiment.</li> <li><code>bunker</code>: Field site of the experiment. Bunker IDs 1 and 2 correspond to field sites A and B, respectively.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h_sledge_top</code>: Distance from sample top surface to the sled in mm.</li> <li><code>h_wl_top</code>: Distance from sample top surface to weak layer in mm.</li> <li><code>h_wl_notch</code>: Distance from the notch root to the weak layer in mm.</li> <li><code>rc_right</code>: Critical cut length in mm, measured on the front side of the sample.</li> <li><code>rc_left</code>: Critical cut length in mm, measured on the back side of the sample.</li> <li><code>rc</code>: Mean of <code>rc_right</code> and <code>rc_left</code>.</li> <li><code>densities</code>: List of density measurements in kg/m^3 for each distinct slab layer of each sample.</li> <li><code>densities_mean</code>: Daily mean of <code>densities</code>.</li> <li><code>layers</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>layers_mean</code>: Daily mean of <code>layers</code>.</li> <li><code>surface_lineload</code>: Surface line load of added surface weights in N/mm.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>notes</code>: Additional notes regarding the experiment or observations.</li> <li><code>L</code>: Length of the slab–weak-layer assembly in mm.</li> </ul> <h3><code>df_legacy.pkl</code></h3> <div>Contains robustness data such as radii of curvature, slope inclination, and various geometrical measurements.</div> <ul> <li><code>#</code>: Record number.</li> <li><code>rc</code>: Critical cut length in mm.</li> <li><code>slope_incl</code>: Inclination of the slope in degrees.</li> <li><code>h</code>: Slab height in mm.</li> <li><code>density</code>: Mean slab density in kg/m^3.</li> <li><code>L</code>: Lenght of the slab–weak-layer assembly in mm.</li> <li><code>collapse_height</code>: Weak-layer height reduction through collapse.</li> <li><code>layers_mean</code>: 2D array with layer density (kg/m^3) and layer thickness (mm) pairs for each distinct slab layer.</li> <li><code>wl_thickness</code>: Weak-layer thickness in mm.</li> <li><code>surface_lineload</code>: Surface line load from added weights in N/mm.</li> </ul> <p>For more detailed information on the datasets, refer to the paper or the documentation provided within the Jupyter notebook.</p> <h2>License</h2> <div>This work is licensed under a <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</div> <p> </p> <div>You are free to:</div> <ul> <li><strong>Share</strong> — copy and redistribute the material in any medium or format</li> <li><strong>Adapt</strong> — remix, transform, and build upon the material for any purpose, even commercially.</li> </ul> <div>Under the following terms:</div> <div> <ul> <li><strong>Attribution</strong> — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.</li> </ul> </div> <h2>Citation</h2> <div>Please cite the following paper if you use this analysis or the accompanying datasets:</div> <div> <ul> <li>Adam, V., Bergfeld, B., Weißgraeber, P. van Herwijnen, A., Rosendahl, P.L., Fracture toughness of mixed-mode anticracks in highly porous materials. <em>Nature Communincations</em> <strong>15</strong>, 7379 (2024). https://doi.org/10.1038/s41467-024-51491-7</li> </ul> </div>
Inlists for the paper "Mixed-mode coupling in the Red Clump: I. Standard single star models"
<p>This repository contains the inlists and run_star_extras used for the paper "Mixed-mode coupling in the Red Clump: I. Standard single star models" by Walter E. van Rossem, Andrea Miglio, and Josefina Montalban for use with MESA-11701. The grid cycles through masses first (0.7, 1.0, 1.5, 2.3, 3.0 msol) and then metallicity ([Fe/H] = -1.0, -0.5, 0.0, 0.25, 0.4).</p> <p>Runs 0000-0004 have initial [Fe/H] = -1.0 and masses 0.7, 1.0, 1.5, 2.3, 3.0 solar masses respectively. The next five runs (0005-0009) have [Fe/H] = -0.5 and the same order of masses, and so on.</p> <p>The previous version had an error in the calculation for the non-parallel approximation and was missing a squareroot in the subroutine <code>calc_dlnc_ds_s0_part_ap</code> when calculating <code>Nred_km1</code> and <code>Nred_k</code>.</p>
Fracture resistance dataset of composites under mixed-mode non-proportional loading
<p>Fracture resistance dataset of composites under mixed-mode non-proportional loading</p>
Mixed-mode fracture: Combination of Arcan fixture and stereo-DIC
<p>data and codes used for article titled "Mixed-mode fracture: Combination of Arcan fixture and stereo-DIC"</p>
Probing fossil magnetic field effects in the core of evolved low-mass stars using mixed-mode frequencies
<p>The recent discovery of the moderate differential rotation between the core and the envelope of intermediate-mass (IM) main-sequence and evolved stars, and the population of IM red giants presenting a surprisingly low-amplitude of their mixed modes (i.e. modes that behave as acoustic modes in their external envelope and as gravity modes in their core) could both be the signature of a strong magnetic field trapped inside the radiative regions of IM stars. Indeed, stars more massive than 1.1 solar mass are known to develop a convective core during their main sequence. The field generated by the dynamo triggered by this convection could be the progenitor of a strong fossil magnetic field trapped inside the core of the star for the rest of its evolution. In this context, the mixed modes observed thanks to space-based asteroseismology can constitute an excellent probe of the deepest layers in IM evolved stars: such magnetic fields may impact their propagation inside the core of these stars, and these perturbations should be visible in asteroseismic data. To unravel which constraints can be obtained from these observations, we theoretically investigate the effects of a plausible mixed magnetic field with various amplitudes on the mixed-mode frequencies of red giants. Applying a perturbative method, we estimate the magnetic splitting of the frequencies of simulated mixed dipolar modes that depends on the magnetic field strength and its configuration. A complete asymptotic analysis is derived, showing the potential of asteroseismology to probe the magnetism at each depth as this is done for stellar rotation. The effects of the mass and the metallicity of the stars are also explored. Finally, we infer an upper limit for the strength of the field and the associated lower limit for the timescale of its action to redistribute angular momentum in stellar interiors.</p>
Environmental Factors on DFUs Incidence - a Mixed-mode Survey
ClinicalTrials.gov study NCT05464524. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.