Jove
Visualize
Contact Us

Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.6K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.6K
Current Growth And Decay In RL Circuits01:30

Current Growth And Decay In RL Circuits

3.9K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
3.9K
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

473
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....
473
Second Order systems II01:18

Second Order systems II

142
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
142
Multimachine Stability01:25

Multimachine Stability

210
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
210
Dynamic Equilibrium02:20

Dynamic Equilibrium

52.3K
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
52.3K

You might also read

Related Articles

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

Sort by
Same author

Detecting disturbances in network-coupled dynamical systems with machine learning.

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

Dynamics on networks with higher-order interactions.

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

Synchronization of phase oscillators on complex hypergraphs.

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

Multistability in coupled oscillator systems with higher-order interactions and community structure.

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

Hypergraph assortativity: A dynamical systems perspective.

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

A self-organized critical model and multifractal analysis for earthquakes in Central Alborz, Iran.

Scientific reports·2022
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 Experiment Video

Updated: Aug 8, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K

Stochastic and deterministic dynamics in networks with excitable nodes.

M Rahimi-Majd1, J G Restrepo2, M N Najafi3

  • 1Department of Physics, Shahid Beheshti University, 1983969411 Tehran, Iran.

Chaos (Woodbury, N.Y.)
|March 1, 2023
PubMed
Summary
This summary is machine-generated.

Nonlinear transfer functions in excitable networks cause discontinuous phase transitions and hysteresis, unlike linear models. This highlights the importance of considering nonlinearity for accurate collective dynamics predictions in complex systems.

More Related Videos

Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

9.6K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.0K

Related Experiment Videos

Last Updated: Aug 8, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.2K
Sealable Femtoliter Chamber Arrays for Cell-free Biology
13:44

Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

9.6K
Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
10:45

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays

Published on: May 29, 2017

10.0K

Area of Science:

  • Complex Systems
  • Nonlinear Dynamics
  • Network Science

Background:

  • Networks of excitable systems model diverse phenomena across biology, social sciences, and physics.
  • Continuous phase transitions are common in these models, often assuming linear node transfer functions for small inputs.
  • Previous models implicitly relied on linearity, potentially oversimplifying real-world dynamics.

Purpose of the Study:

  • To investigate the impact of cooperative excitations and nonlinear transfer functions on the collective dynamics of excitable networks.
  • To understand how nonlinearity qualitatively alters phase transitions and introduces phenomena like hysteresis.
  • To analyze finite-size effects in networks with nonlinear dynamics.

Main Methods:

  • Development of a mean-field theory to analyze collective dynamics using a one-dimensional map.
  • Theoretical analysis of finite-size effects by examining the behavior of single-node initial excitations in large networks.
  • Numerical simulations to complement theoretical findings and validate the model's predictions.

Main Results:

  • Introduction of any nonlinearity into the transfer function qualitatively changes system dynamics, leading to discontinuous phase transitions and hysteresis.
  • The mean-field theory successfully captures the essential features of the dynamics.
  • Nonlinear transfer functions create a rich effective phase diagram for finite networks, deviating from predictions of linear models.

Conclusions:

  • Nonlinearities in excitable networks fundamentally alter collective dynamics, inducing discontinuous transitions and hysteresis.
  • Mean-field theory and finite-size analysis provide valuable insights into these complex behaviors.
  • Researchers must carefully consider nonlinear effects and avoid assumptions of noncooperative excitations when modeling excitable systems.