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Related Concept Videos

Convergent Evolution01:54

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Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Kinematic Equations - II01:17

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The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
Kinematic Equations - III01:18

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The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
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Kinematic Equations: Problem Solving01:15

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When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
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Related Experiment Video

Updated: Jul 3, 2026

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques
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At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

Published on: February 11, 2020

Quantifying the complexity of bat wing kinematics.

Daniel K Riskin1, David J Willis, José Iriarte-Díaz

  • 1Department of Ecology and Evolutionary Biology, Brown University, Providence, RI 02912, USA. dkr8@brown.edu

Journal of Theoretical Biology
|July 16, 2008
PubMed
Summary

Proper orthogonal decomposition (POD) reveals that bat flight dimensional complexity is conserved across speeds. Optimal marker placement, focusing on hindlimb and digits III/IV, enhances kinematic analysis for simplified flight models.

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A Computational Method to Quantify Fly Circadian Activity
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A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

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A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

Area of Science:

  • Biomechanics
  • Animal locomotion
  • Fluid dynamics

Background:

  • Animal body motion (kinematics) is complex, especially with flexible parts interacting with fluids.
  • Traditional methods limit discovery by pre-selecting important variables, constraining analysis of influential factors.

Purpose of the Study:

  • Apply proper orthogonal decomposition (POD) to quantify kinematic dimensional complexity in bat flight.
  • Determine if dimensional complexity changes with flight speed.
  • Identify optimal body markers for capturing dimensional complexity.
  • Inform simplified reconstructions of bat flight kinematics.

Main Methods:

  • Utilized proper orthogonal decomposition (POD) as a metric for dimensional complexity.
  • Measured 17 kinematic markers (20 joint angles) on a bat (Cynopterus brachyotis) in a wind tunnel across nine speeds.
  • Analyzed subsets of markers to assess their contribution to dimensional complexity.

Main Results:

  • Dimensional complexity remained constant across different flight speeds, despite kinematic variations.
  • Increased marker count improved resolution, but with diminishing returns.
  • Optimal marker placement included the hindlimb and specific points on digits III and IV.
  • POD identified three correlated joint groups, explaining 14/20 joint angles.

Conclusions:

  • The relative importance of kinematic dimensions for reconstruction is conserved across flight speeds.
  • Strategic marker selection, focusing on hindlimb and digits, is crucial for efficient kinematic analysis.
  • Identified joint coordination groups provide a framework for bat flight modeling and experimental studies.