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

Deformation of Member under Multiple Loadings01:11

Deformation of Member under Multiple Loadings

208
When a rod is made of different materials or has various cross-sections, it must be divided into parts that meet the necessary conditions for determining the deformation. These parts are each characterized by their internal force, cross-sectional area, length, and modulus of elasticity. These parameters are then used to compute the deformation of the entire rod.
In the case of a member with a variable cross-section, the strain is not constant but depends on the position. The deformation of an...
208
Temperature Dependent Deformation01:12

Temperature Dependent Deformation

183
In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added...
183
Deformations in a Symmetric Member in Bending01:18

Deformations in a Symmetric Member in Bending

252
When analyzing the deformation of a symmetric prismatic member subjected to bending by equal and opposite couples, it becomes clear that as the member bends, the originally straight lines on its wider faces curve into circular arcs, with a constant radius centered at a point known as Point C. This phenomenon helps to understand the stress and strain distribution within the member more clearly.
When the member is segmented into tiny cubic elements, it is observed that the primary stress...
252
Transformation of Plane Strain01:12

Transformation of Plane Strain

227
When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
Under plane strain conditions, typical for members where one dimension significantly exceeds the others, deformations and resultant strains are...
227
Three-Dimensional Analysis of Strain01:29

Three-Dimensional Analysis of Strain

280
Three-dimensional strain analysis is crucial for understanding how materials deform under stress, particularly in elastic, homogeneous materials. This method employs principal stress axes to simplify complex stress states into more understandable forms. Subjected to stress, a small cubic element within a material either expands or contracts along these axes, transforming into a rectangular parallelepiped. This transformation effectively illustrates the material's deformation. The principal...
280
Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

418
One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
418

You might also read

Related Articles

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

Sort by
Same author

Intelligent Veterinary Disease Management Driven by Knowledge Graph for Conservation Breeding of Captive Forest Musk Deer.

Veterinary sciences·2026
Same author

Optimizing the genomic bit budget: an information-theoretic framework for trait-enriched genotyping and stratified screening in <i>Theobroma cacao</i>.

Horticulture research·2026
Same author

Pharmacokinetics, safety, and efficacy of fuzuloparib in combination with abiraterone acetate and prednisone in patients with metastatic castration-resistant prostate cancer: a phase 1 dose escalation and expansion study.

BMC cancer·2026
Same author

Constraining near-term projections of the South Asian high and Afro-Asian summer monsoon rainfall.

Nature communications·2026
Same author

Keypoint-Based Forest Musk Deer Behavioral Recognition Method.

Animals : an open access journal from MDPI·2026
Same author

Affinity Capillary Electrophoresis for the Study of Biomolecular Interactions: Recent Advances and Future Perspectives.

Journal of separation science·2026

Related Experiment Video

Updated: Aug 27, 2025

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.1K

Cluster synchronization induced by manifold deformation.

Ya Wang1, Dapeng Zhang1, Liang Wang1

  • 1School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.

Chaos (Woodbury, N.Y.)
|October 1, 2022
PubMed
Summary

Pinning control synchronizes chaotic oscillators into two clusters. The generalized master stability function (MSF) method predicts pinned oscillator synchronization but fails for unpinned ones due to deformed manifolds.

More Related Videos

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K
Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.5K

Related Experiment Videos

Last Updated: Aug 27, 2025

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy
07:53

Group Synchronization During Collaborative Drawing Using Functional Near-Infrared Spectroscopy

Published on: August 5, 2022

2.1K
Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

1.4K
Quantifying Intermembrane Distances with Serial Image Dilations
07:45

Quantifying Intermembrane Distances with Serial Image Dilations

Published on: September 28, 2018

6.5K

Area of Science:

  • Complex systems
  • Nonlinear dynamics
  • Network science

Background:

  • Cluster synchronization is crucial in complex networks.
  • Pinning control is a method to manipulate synchronization.
  • The generalized master stability function (MSF) is a common analytical tool.

Purpose of the Study:

  • To investigate pinning control of cluster synchronization in globally connected chaotic oscillator networks.
  • To analyze the effectiveness of the generalized MSF method in predicting synchronization behaviors.
  • To understand the underlying reasons for discrepancies in synchronization prediction.

Main Methods:

  • Numerical simulations of globally connected chaotic oscillator networks.
  • Application and analysis of the generalized master stability function (MSF).
  • Phase space trajectory analysis to examine synchronization manifolds.

Main Results:

  • Exceeding a critical pinning strength leads to two synchronized clusters: pinned and unpinned oscillators.
  • The generalized MSF accurately predicts synchronization for pinned oscillators.
  • The generalized MSF fails to predict synchronization for unpinned oscillators due to deformed synchronization manifolds.
  • Similar phenomena observed in symmetric networks and neural oscillator networks.

Conclusions:

  • The generalized MSF method has limitations in predicting synchronization for all nodes in certain complex network configurations.
  • Deformed synchronization manifolds in unpinned oscillators explain the failure of the generalized MSF.
  • Findings offer insights into manipulating synchronization in complex systems and complement existing analytical methods.