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165 results for “mechanical properties”
Data from: Diverse and complex muscle spindle afferent firing properties emerge from multiscale muscle mechanics
<p>Despite decades of research, we lack a mechanistic framework capable of predicting how movement-related signals are transformed into the diversity of muscle spindle afferent firing patterns observed experimentally, particularly in naturalistic behaviors. Here, a biophysical model demonstrates that well-known firing characteristics of mammalian muscle spindle Ia afferents – including movement history dependence, and nonlinear scaling with muscle stretch velocity – emerge from first principles of muscle contractile mechanics. Further, mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal how motor commands to the muscle (alpha drive) versus muscle spindle (gamma drive) can cause highly variable and complex activity during active muscle contraction and muscle stretch that defy simple explanation. Depending on the neuromechanical conditions, the muscle spindle model output appears to "encode" aspects of muscle force, yank, length, stiffness, velocity, and/or acceleration, providing an extendable, multiscale, biophysical framework for understanding and predicting proprioceptive sensory signals in health and disease.</p>
Dataset of "Pore-scale simulation of the impact of CO2-Water-Mineral Interaction on Rock Permeability and Mechanical Properties"
<p>This dataset supports the research study 'Pore-scale simulation of the impact of CO2-Water-Mineral Interaction on Rock Permeability and Mechanical Properties' by B. Yang, T. Xu, Z. Jiang, and H. Zhu.</p>
Data for Study of Thermal and Physical Mechanic Properties of CEB-reused PET
<p>Source for obtaintion of Figure 2, graphic of elemental analysis and Figure 5, Graphic of results for heat transfer model with the sample, where the "x" axis is time (in hours), and the "y" axis is temperature (in celsius degrees).</p>
Dataset for publication 7. Eslam El-Seidy, Mehdi Chougan, Yazeed A. AI-Noaimat, Mazen J. Al-Kheetan, Seyed Hamidreza Ghaffar. (2024). The impact of waste brick and geo-cement aggregates as sand replacement on the mechanical and durability properties of alkali–activated mortar composites, Results in Engineering, doi: https://doi.org/10.1016/j.rineng.2024.101797
Open the record for dataset details and reuse information.
Data for Impact of nano-dopants on the mechanical and physical properties of magnesium oxychloride cement composites – experimental assessment
<p>The importance of the World’s reduction of CO2 emissions is inevitable as CO2 is one of the main contributors to climate change. As the Portland cement (PC) industry is a non-negligible contributor to the World’s CO2 emissions, a sustainable PC substitute must be implemented. Previous scientific studies suggest that magnesium oxychloride cement (MOC) – an eco-friendly binder based on reactive magnesia – could be a suitable alternative. This contribution studies the influence of various nanoadditives on the mechanical and physical properties of MOC-based composites. The nanoadditives introduced to the MOC composite were the following: multi-walled carbon nanotubes, oxidized multi‑walled carbon nanotubes, graphene oxide, graphene nanoplatelets, and alumina nanosheets. Ahead of the composite preparation, the nanoadditives were analysed with a wide spectrum of analytical methods to study their composition and microstructure in detail. The used amount of nanoadditive was 1.0 wt.%, relative to the cement paste weight. After a curing period of 28 days, the prepared composite samples underwent a comprehensive analysis of their microstructure, phase, and chemical composition, as well as micro- and macrostructural parameters and mechanical properties. Additionally, the research studied the effects of nanoadditives on the hygric properties and water resistance of the MOC composites after 24-h immersion in water. The findings revealed a noteworthy increase in mechanical performance with the addition of nano-dopants. The highest compressive strength of 85.4 MPa was obtained for the MOC composite enriched with the addition of oxidized multi-walled carbon nanotubes. MOC doped with alumina nanosheets exhibited the best flexural strength (20.99 MPa) and dynamic modulus of elasticity (38.85 GPa). The resistance of MOC composites to deterioration in water was also improved by all the nanoadditives investigated. The softening coefficient determined for samples immersed in water for 24 h varied from 62.7 to 74.8. The best resistance to water degradation was obtained for MOC with alumina nanoadditive, which showed a softening coefficient value about 23.2% higher than that of the reference sample. Thus, the incorporation of nanomaterials as functional nanoadditives in MOC-based composites shows promising advances in both mechanical properties and water resistance, contributing to the development of environmentally sustainable building materials.</p>
Finite element data collected and Machine learning algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several ML algorithms (Regression Tree, Random Forest, Gradient Boosting and Artificial Neural Network), the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Figure 1 from: Dasari S (2016) Studying the effect of Ruthenium on High Temperature Mechanical Properties of Nickel Based Superalloys and Determining the Universal Behavior of Ruthenium at Atomic Scale with respect to alloying elements, Stress and Temperature. Research Ideas and Outcomes 2: e10714. https://doi.org/10.3897/rio.2.e10714
Figure 1 - Chemical composition of TMS 138 alloy.
Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials
<p>This repository contains data to</p> <p>Characterization and impact of fiber size variability on the mechanical properties of fiber networks with an application to paper materials</p> <p>In prep. 2021.</p> <p> </p> <p>There are a few key files to note:</p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/mainArticleE.m?versionId=e434d0da-db30-4588-a807-de72c4187468">mainArticleE.m </a><br> Generates a number of plots from the manuscript, and also contains calls to the functions that can be used to study how the copula formulation can be used and worked with.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a><br> Shows the steps performed in the fitting and sampling that are presented in the manuscript.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/data.zip">data.zip </a><br> Contains all of the data. Raw machine outputs, condensed outputs (several runs collected into a single file), the ESI simulations and the result files used to generate the plots. The full solution files are not included as they would have taken way too much space. However, the input files can be found in each repository.</li> </ul> <p> </p> <ul> <li><a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/v1.0.zip?versionId=8134396c-687b-4c31-a220-d27676f391cf">v1.0.zip </a><br> Output from <a href="https://zenodo.org/api/files/c161a833-8968-4d9f-bc1e-1bd6ea8a89ad/calculationSequenceForArticle.m?versionId=be372e6e-6eda-4ba5-b78b-2c8966de45f0">calculationSequenceForArticle.m </a>just before submission.</li> </ul> <p> </p> <p>The FEM files are not included (MyPacking.exe - an internal tool I do not own the rights to, and the linked ANSYS executable which I likewise do not own the rights to).</p> <p>Also absent is the "pulpDistr" package (will be a seperate ZENODO submission)-</p> <p> </p> <p> </p> <p>Should something not work, please do get in touch.</p> <p> </p> <p>/A. Brandberg, 2021-08-10</p> <p> </p> <p> </p>
Structural, electronic and mechanical properties of double core carbon nanothreads (Supplemental Files)
<p>The structures shown here are examples of the double core nanothreads studied in the article entitled “Structural, electronic and mechanical properties of double core carbon nanothreads” published on Carbon (https://doi.org/10.1016/j.carbon.2023.118387) and authored by Caio M. Miliante, J. P. Dotto de Matos and André R. Muniz. These structures are shared with the ".cif" file format generated from the VESTA program (https://jp-minerals.org/vesta/en), which can also be used to visualize them.</p>
Mechanical Properties of the Internal Limiting Membrane and Intraoperative Utility of Brilliant Blue g (Bbg) and Indocyanine Green (Icg) Assisted Chromovitrectomy
ClinicalTrials.gov study NCT01485575. IPD Sharing: UNDECIDED. Countries: 2. Publications: 0.
Clinical Outcomes, Viscoelastic Properties and Central Pain Mechanisms After Eccentric Training in Neck/Shoulder Pain
ClinicalTrials.gov study NCT03474705. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Relationship Between Mechanical Properties of Respiratory and Lower Extremity Muscles and Other Parameters in Multiple Sclerosis
ClinicalTrials.gov study NCT07202780. IPD Sharing: NO. Countries: 1. Publications: 0.
Characterization of Mechanical Tissue Properties in Patients With Pancreatic, Liver, or Colon Cancer
ClinicalTrials.gov study NCT03137706. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effect of Autoclave Cycles on the Mechanical Properties and Surface Roughness of NITI Archwires
ClinicalTrials.gov study NCT05860738. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Tendon Mechanical Properties and Morphological Characteristics of Human Bicep and Tricep Muscles: An In Vivo Ultrasonographical Study
ClinicalTrials.gov study NCT00173979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Correlation Between Oocyte and Embryo Mechanical Properties on Embryo Development and Clinical Pregnancy After In Vitro Fertilization
ClinicalTrials.gov study NCT02530892. IPD Sharing: UNDECIDED. Countries: 3. Publications: 0.
Evaluating Mechanical Properties of Post-Mastectomy Skin Flaps to Estimate Reconstruction Risks, the EMPOWER Study
ClinicalTrials.gov study NCT06584396. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Correlation of Thoracolumbar Fascia Mechanical Properties and Compensate Mechanisms of Asymmetric Skeletal Muscle
ClinicalTrials.gov study NCT02459366. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Characterization of the Mechanical Properties of the Hamstring Muscle Group in Female.
ClinicalTrials.gov study NCT05802277. IPD Sharing: NO. Countries: 1. Publications: 0.
Mechanical Properties of Peripheral and Accessory Respiratory Muscles in Chronic Obstructive Pulmonary Disease Patients
ClinicalTrials.gov study NCT06201403. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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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.