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841 results for “vibrations”
Whole Body Vibration and the Brain in OSA
ClinicalTrials.gov study NCT04009486. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Effect of Whole-body Vibration Training
ClinicalTrials.gov study NCT04273308. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Vibration Approach Functions in Upper Extremities for People After Stroke
ClinicalTrials.gov study NCT05969249. IPD Sharing: NO. Countries: 0. Publications: 0.
Whole Body Vibration in Individuals Submitted to ACL
ClinicalTrials.gov study NCT02609971. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Effects of Vibration on Hypoxia
ClinicalTrials.gov study NCT01245491. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effect of Whole-body Vibration on Postural Stability, Muscle Flexibility and Lower Limb Power in Young Basketball Players with Generalized Joint Hypermobility
ClinicalTrials.gov study NCT06691737. IPD Sharing: Not stated. Countries: 0. Publications: 0.
The Effect of Random Vibrations on Perception and Balance
ClinicalTrials.gov study NCT02989831. IPD Sharing: NO. Countries: 0. Publications: 0.
Whole Body Vibrations on Functional Capacity, Muscular Strength, and Biochemical Profile in Elders
ClinicalTrials.gov study NCT03030456. IPD Sharing: NO. Countries: 0. Publications: 0.
Effects of Whole-Body Vibration and High Impact Exercises in Postmenopausal Women
ClinicalTrials.gov study NCT03910348. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Vibration Enhances Diabetic ULCER Healing
ClinicalTrials.gov study NCT04275804. IPD Sharing: NO. Countries: 0. Publications: 0.
Vibrational Therapy to Improve Gait and Balance in Parkinson's Disease
ClinicalTrials.gov study NCT03872115. IPD Sharing: NO. Countries: 0. Publications: 0.
Vibrational Neutron Scattering Spectra for Undoped and Doped P3HT
Open the record for dataset details and reuse information.
ChIP-seq for identifying molecules associated with sound vibration in plant
GEO Series GSE133320. Arabidopsis thaliana. 36 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Prognostics Health Management of Electronic Systems Under Mechanical Shock and Vibration Using Kalman Filter Models and Metrics
Structural damage to ball grid array interconnects incurred during vibration testing has been monitored in the prefailure space using resistance spectroscopy-based state space vectors, rate of change of the state variable, and acceleration of the state variable. The technique is intended for condition monitoring in high reliability applications where the knowledge of impending failure is critical and the risks in terms of loss of functionality are too high to bear. Future state of the system has been estimated based on a second-order Kalman Filter model and a Bayesian Framework. The measured state variable has been related to the underlying interconnect damage in the form of inelastic strain energy density. Performance of the prognostic health management algorithm during the vibration test has been quantified using performance evaluation metrics. The method- ology has been demonstrated on leadfree area-array electronic assemblies subjected to vibration. Model predictions have been correlated with experimental data. The presented approach is applicable to functional systems where corner interconnects in area-array packages may be often redundant. Prognostic metrics including α − λ precision, β accuracy, and relative accuracy have been used to assess the performance of the damage proxies. The presented approach enables the estimation of residual life based on level of risk averseness.
Multiomics coupled with vibrational spectroscopy identify early mechanisms of experimental aortic valve stenosis [RNA]
GEO Series GSE267949. Oryctolagus cuniculus. 27 samples. Type: Expression profiling by high throughput sequencing.
Multiomics coupled with vibrational spectroscopy identify early mechanisms of experimental aortic valve stenosis [miRNA]
GEO Series GSE267951. Oryctolagus cuniculus. 16 samples. Type: Non-coding RNA profiling by high throughput sequencing.
Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions
<p><strong>Description:</strong></p> <p>This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.</p> <p>The data were collected using <a href="https://www.skf.com/it/products/rolling-bearings/roller-bearings/spherical-roller-bearings/productid-22240%20CCK%2FW33">SKF 22240 CCK/W33 spherical roller bearings</a> through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (<a href="https://www.dimeas.polito.it/la_ricerca/gruppi/ised_industrial_systems_engineering_and_design">ISED Research Group</a>). The technical details of the test rig can be found in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>.</p> <p>The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.</p> <p><strong>Dataset Structure:</strong></p> <p>The dataset is organized into four folders:</p> <ul> <li><code>Undamaged</code></li> <li><code>InnerRaceDamage</code></li> <li><code>OuterRaceDamage</code></li> <li><code>RollerDamage</code></li> </ul> <p>The <code>Undamaged</code> folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the <strong>inner race</strong> (IR), <strong>outer race</strong> (OR), or <strong>roller elements</strong> (B). More detailed information about the defect geometry and location can be found in the following publications:</p> <ul> <li><a href="https://www.mdpi.com/1424-8220/23/1/211">Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/4/2038">Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring</a></li> <li><a href="https://www.mdpi.com/2076-3417/13/22/12458">Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks</a></li> </ul> <p>Each folder contains <code>.mat</code> files named according to the following format:</p> <p><code>(<em>Nominal_Rotation_Speed</em>)rpm_(<em>Radial_Force</em>)kN_(<em>Axial_Force</em>)kN.mat</code></p> <p>Where:</p> <ul> <li><strong>Nominal_Rotation_Speed</strong> is the machine's nominal rotational speed</li> <li><strong>Radial_Force</strong> is the radial force applied to the bearing</li> <li><strong>Axial_Force</strong> is the axial force applied to the bearing</li> </ul> <p>The dataset includes measurements from 10 different nominal rotation speeds and four load conditions, one of which includes an axial load. In some cases, "ramp" files are included, containing data where the rotational speed was linearly varied during the test.</p> <p>Each <code>.mat</code> file contains multiple structures, depending on the type of test. Each structure is labeled as <code>Signal_</code> followed by a number (0, 1, 2, 3, 4), where each represents a specific signal extracted during the test. There is no fixed correspondence between the signal number and the type of measurement. For example, <code>Signal_2</code> does not always represent the accelerometer signal. Users are encouraged to inspect the <code>y_values.quantity</code> field to identify the signal's unit and nature. For instance, if <code>y_values.quantity.label</code> shows "g", the signal corresponds to an accelerometric measurement.</p> <p>All signals have been exported using the <strong>MKS system</strong>, so <code>y_values.values</code> contains data in units of m/s² for acceleration signals. To convert the values to the unit indicated in <code>y_values.quantity.label</code>, users can apply the multiplication factor and offset provided in <code>y_values.quantity.unit_transformation</code>. In the case of accelerometric data, the multiplication factor is 0.1020, converting <code>y_values.values</code> from m/s² to g.</p> <p>A more detailed description of the data structure can be found in the Test.Lab documentation.</p> <p>In addition to acceleration data, the files include temperature signals, tacho sensor signals measuring shaft speed, and tacho impulse signals. In some cases, there is also a signal measured in "N", representing the frictional force generated by the bearings (as detailed in <a href="https://www.mdpi.com/2075-1702/10/1/54">this Paper</a>). This friction force signal is not always present, as the load cell could go into overload during certain tests.</p> <p><strong>Sensor Data Organization:</strong></p> <p>Acceleration and temperature data are presented in tables with four columns, each corresponding to one of the four bearings. In the damaged condition tests, the damaged bearing is always the one corresponding to sensor 4 (i.e., <code>y_values.values(:, 4)</code>).</p>
Passive self-turning vibration neutraliser
<p>The videos showcase the <strong>passive self-turning vibration neutraliser</strong> developed in the works of Nehemy et al. (2024) and Nehemy et al. (2023). This neutraliser is designed with <strong>two distinct tuning frequencies</strong> that cause it to rotate to either 0° or 90°, depending on the excitation frequency applied at its base.</p> <p>In each video, the neutraliser is initially positioned at either 0° or 90° and is then excited at the tuning frequency associated with the opposite position. This excitation induces the neutraliser to passively rotate to the alternative orientation, driven solely by the applied frequency. The rotation mechanism is entirely passive, leveraging the nonlinear coupling between the system's degrees of freedom to achieve automatic adjustment based on the excitation frequency.</p>
Vibration Response Imaging (VRI) in Children With Acute Respiratory Symptoms
ClinicalTrials.gov study NCT00992966. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows
<p>The dataset shown in the following manuscript is made.</p> <p>Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows</p>
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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.