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

Classification of Systems-II01:31

Classification of Systems-II

433
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
433
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

849
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
849
Transmission-Line Differential Equations01:26

Transmission-Line Differential Equations

906
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
906
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.7K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.7K
Linear time-invariant Systems01:23

Linear time-invariant Systems

813
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
813
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

734
Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
734

You might also read

Related Articles

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

Sort by
Same author

Flatness-based control for generalized synchronization of chaotic systems with large dissipation and dimension mismatch.

Chaos (Woodbury, N.Y.)·2025
Same author

Local Predictors of Explosive Synchronization with Ordinal Methods.

Entropy (Basel, Switzerland)·2025
Same author

Generalized synchronization mediated by a flat coupling between structurally nonequivalent chaotic systems.

Chaos (Woodbury, N.Y.)·2023
Same author

Unveiling the Connectivity of Complex Networks Using Ordinal Transition Methods.

Entropy (Basel, Switzerland)·2023
Same author

Observability analysis and state reconstruction for networks of nonlinear systems.

Chaos (Woodbury, N.Y.)·2022
Same author

Topological synchronization of chaotic systems.

Scientific reports·2022

Related Experiment Video

Updated: Dec 31, 2025

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

1.8K

Topological characterization versus synchronization for assessing (or not) dynamical equivalence.

Christophe Letellier1, Sylvain Mangiarotti2, Irene Sendiña-Nadal3

  • 1Normandie University, CORIA, Avenue de l'Université, 76800 Saint-Etienne du Rouvray, France.

Chaos (Woodbury, N.Y.)
|January 8, 2020
PubMed
Summary

Synchronization is not a reliable method for validating scientific models using experimental data. Even topologically different models can synchronize, leading to incorrect conclusions about model accuracy.

More Related Videos

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.2K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.0K

Related Experiment Videos

Last Updated: Dec 31, 2025

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

1.8K
Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.2K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.0K

Area of Science:

  • Chemistry
  • Chemical Engineering
  • Systems Biology

Background:

  • Model validation is crucial for scientific accuracy but often relies on subjective visual inspection.
  • Synchronization has been proposed as an objective technique for model validation.
  • Jack Hudson's group conducted electrodissolution experiments providing valuable data.

Purpose of the Study:

  • To critically evaluate the suitability of synchronization as a model validation technique.
  • To analyze the topological properties of models and their synchronization behavior.
  • To reassess model validation methods in light of experimental data.

Main Methods:

  • Topological analysis of abstract chemical reaction systems.
  • Application of synchronization techniques to model validation.
  • Comparison of model synchronization with experimental data from electrodissolution.

Main Results:

  • Synchronization was achievable for topologically distinct global models.
  • This synchronization did not necessarily reflect accurate model representation.
  • The study highlights limitations of synchronization in model validation.

Conclusions:

  • Synchronization is not a recommendable technique for validating scientific models.
  • Visual inspection, despite its limitations, may be more informative than synchronization alone.
  • Rethinking model validation strategies is necessary for robust scientific discovery.