Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

390
Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
Next,...
390
Kinematic Equations - III01:18

Kinematic Equations - III

7.7K
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.
Using the kinematic equations,...
7.7K
Kinematic Equations - II01:17

Kinematic Equations - II

9.6K
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...
9.6K
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.5K
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...
12.5K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

151
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
151
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.4K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.4K

You might also read

Related Articles

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

Sort by
Same author

Exploring the structure of the school curriculum with graph neural networks.

Journal of computational social science·2025
Same author

Bayesian inference of transition matrices from incomplete graph data with a topological prior.

EPJ data science·2023
Same author

Locating community smells in software development processes using higher-order network centralities.

Social network analysis and mining·2023
Same author

Predicting variable-length paths in networked systems using multi-order generative models.

Applied network science·2023
Same author

Analysing Time-Stamped Co-Editing Networks in Software Development Teams using git2net.

Empirical software engineering·2021
Same author

Memory order decomposition of symbolic sequences.

Physical review. E·2021
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Jul 13, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

Inference of time-ordered multibody interactions.

Unai Alvarez-Rodriguez1,2, Luka V Petrović2, Ingo Scholtes2,3

  • 1University of Deusto, 48007 Bilbao, Spain.

Physical Review. E
|October 18, 2023
PubMed
Summary
This summary is machine-generated.

We developed time-ordered multibody interactions to analyze complex systems with temporal and many-body dependencies. Our method efficiently extracts these interactions from data, robustly handling statistical errors.

More Related Videos

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.8K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.0K

Related Experiment Videos

Last Updated: Jul 13, 2025

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.8K
Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects
06:36

Author Spotlight: Insights into the Analysis of Human Interaction with 3D Virtual Objects

Published on: October 18, 2024

1.0K

Area of Science:

  • Complex Systems Science
  • Statistical Physics
  • Network Science

Background:

  • Complex systems often exhibit both temporal dynamics and intricate many-body interactions.
  • Understanding these coupled dependencies is crucial for modeling and prediction.
  • Existing methods may struggle to capture the full complexity of such systems.

Purpose of the Study:

  • To introduce a novel framework for describing complex systems using time-ordered multibody interactions.
  • To develop an algorithm for extracting these interactions from observational data.
  • To provide a measure for characterizing the complexity of interaction ensembles.

Main Methods:

  • Decomposition of multivariate Markov chain dynamics into time-ordered multibody interactions.
  • Development of a data-driven algorithm for interaction extraction.
  • Introduction of a complexity measure for interaction ensembles.
  • Experimental validation of algorithm robustness and efficiency.

Main Results:

  • Demonstrated decomposition of Markov chain dynamics into time-ordered multibody interactions.
  • Presented a robust and efficient algorithm for extracting these interactions from system dynamics.
  • Showcased the ability to infer parsimonious interaction ensembles.

Conclusions:

  • Time-ordered multibody interactions provide a powerful framework for complex systems.
  • The developed algorithm offers a reliable method for analyzing temporal and many-body dependencies.
  • This approach enhances our ability to model and understand complex system behaviors.