Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

One-Degree-of-Freedom System

One-Degree-of-Freedom System

In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...

Relative Motion Analysis using Rotating Axes-Problem Solving

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...

Absolute Motion Analysis- General Plane Motion

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...

First Law: Particles in Two-dimensional Equilibrium

First Law: Particles in Two-dimensional Equilibrium

Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
Newton's first law tells us about...

Kinematic Equations: Problem Solving

Kinematic Equations: Problem Solving

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...

Kinematic Equations - II

Kinematic Equations - II

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

nphp4 deficiency disrupts testicular homeostasis and impairs male fertility in medaka (Oryzias latipes).

Reproduction (Cambridge, England)·2026

Same author

A Bayesian Stackelberg Game Approach to Remote State Estimation Under SINR-Based DoS Attacks with Incomplete Information.

Sensors (Basel, Switzerland)·2026

Same author

Decoding the RNA Regulatory Network in Medaka (<i>Oryzias latipes</i>) Spermatogenesis: Insights from a Germ Cell Reprogramming Model.

Animals : an open access journal from MDPI·2026

Same author

A peptide-functionalized quantum Dots/MOF nanosheets fluorescence biosensor for glutathione sensing and cellular imaging.

Analytica chimica acta·2026

Same author

A highly sensitive multicolor quantum dots-polylysine@FLISA platform for the simultaneous detection of serum PD-L1, SORL1, and GDF-15 levels.

Analytica chimica acta·2026

Same author

Ego-centered network intervention via weak-tie exclusion.

Chaos (Woodbury, N.Y.)·2025

Search research articles

Related Experiment Video

Updated: Aug 23, 2025

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

The structure inference of flocking systems based on the trajectories.

Jingjie Liang¹, Mingze Qi¹, Kongjing Gu¹

¹College of Science, National University of Defense Technology, Changsha, Hunan 410073, People's Republic of China.

Chaos (Woodbury, N.Y.)

|November 1, 2022

Summary

Inferring swarm interaction structures is challenging. This study introduces Motion Trajectory Similarity to reconstruct these networks from individual movement data, improving swarm behavior analysis.

More Related Videos

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Related Experiment Videos

Last Updated: Aug 23, 2025

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

FIM Imaging and FIMtrack: Two New Tools Allowing High-throughput and Cost Effective Locomotion Analysis

Published on: December 24, 2014

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Area of Science:

Complex Systems
Network Science
Swarm Intelligence

Background:

Understanding swarm dynamics requires knowledge of individual interactions.
Direct observation of these interactions is often infeasible.
Network reconstruction is a fundamental challenge in complex systems research.

Purpose of the Study:

To develop a novel method for inferring interaction structures within swarms.
To reconstruct networks from individual behavior trajectories.
To enhance the understanding of collective movement mechanisms.

Main Methods:

Introduced Motion Trajectory Similarity (MTS) method.
Combined motion trajectory similarity across time series cross-sections.
Identified individuals with highly similar motion states as likely interacting.

Main Results:

MTS reliably infers direct interactions in flocking systems.
Outperformed traditional network inference methods in experiments.
Demonstrated robustness against noise, time delays, and parameter variations.

Conclusions:

MTS offers a new perspective for swarm interaction structure inference.
Facilitates exploration of collective movement mechanisms.
Paves the way for quantifiable and predictable flocking models.