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4 results for “Fall velocity”

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zenodo44/100

Non-relativistic abberation and Doppler shift as experienced in walking with different velocities relative to falling rain

<p>Non-relativistic aberration and Doppler shift as experienced in walking with different velocities relative to falling rain. The scenario on the left (standing) shows the orientation of the umbrella for maximum protection of a person at rest perpendicular to the falling drops. On the right (walking), the situation for a person with an umbrella moving relative to the scenario on the left is shown. The bottom panels depict the related velocities and how they are added. vr is the velocity of the raindrops in the frame of reference of the ground, vr&prime; that in the frame of references of the person with the umbrella, with (right) and without (left) velocity vP with respect to the ground. Note, in the frame of reference of the person the ground moves with &minus;vP. The arrival angle and rate of the raindrops depend on the velocities and are described with aberration and Doppler shift, respectively.</p> <p>Figure adapted from Hoffmann (1983).</p>

opencc-by-4.0Oct 2024View details →
dryad28/100

Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds

<p>1. Seed dispersal by wind is one of the most important dispersal mechanisms in plants. The key seed trait affecting seed dispersal by wind is the effective terminal velocity (hereafter "terminal velocity", Vt), the maximum falling speed of a seed in still air. Accurate estimates of Vt are crucial for predicting intra- and interspecific variation in seed dispersal ability. However, existing methods produce biased estimates of Vt for slow- or fast-falling seeds, fragile seeds, and seeds with complex falling trajectories.</p> <p>2. We present a new video-based method that estimates the falling trajectory and Vt of wind-dispersed seeds. The design involves a mirror that enables a camera to simultaneously record a falling seed from two perspectives. Automated image analysis then determines three-dimensional seed trajectories at high temporal resolution. To these trajectories, we fit a physical model of free fall with air resistance to estimate Vt. We validated this method by comparing the estimated Vt of spheres of different diameters and materials to theoretical expectations, and by comparing the estimated Vt of seeds to measurements in a vertical wind tunnel.</p> <p>3. Vt estimates closely match theoretical expectations for spheres and vertical wind tunnel measurements for seeds. However, our Vt estimates for fast-falling seeds are markedly higher than those in an existing trait database. This discrepancy seems to arise because previous estimates inadequately accounted for seed acceleration. </p> <p>4. The presented method yields accurate, efficient and affordable estimates of the three-dimensional falling trajectory and terminal velocity for a wide range of seed types. The method should thus advance the understanding and prediction of wind-driven seed dispersal.</p>

opencc-zeroJul 2022View details →
dryad28/100

Improving measurements of the falling trajectory and terminal velocity of wind-dispersed seeds

Open the record for dataset details and reuse information.

publicJul 2022View details →
ClinicalTrials.gov24/100

High Velocity Resistance Training Versus Otago Exercise Training on Falling Risk in Elderly

ClinicalTrials.gov study NCT07204808. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →

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DANDI Archive for NWB datasets

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Last verified 2026-04-30Open record

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.

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Last verified 2026-04-29Open record

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neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record