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

BIBO stability of continuous and discrete -time systems

BIBO stability of continuous and discrete -time systems

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.

Multimachine Stability

Multimachine Stability

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:

Transition State Theory

Transition State Theory

Transition-state theory, also known as activated-complex theory, provides a molecular-level explanation of reaction rates in both gas-phase and solution-phase reactions. It extends earlier kinetic models by considering the formation of a short-lived, high-energy configuration during a reaction.The progress of a chemical reaction can be represented using a reaction profile, which plots potential energy against the reaction coordinate. As two reactant molecules approach one another, their...

Entropy Change in Reversible Processes

Entropy Change in Reversible Processes

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.

Network Function of a Circuit

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.

Ladder Diagrams: Complexation Equilibria

Ladder Diagrams: Complexation Equilibria

Ladder diagrams are useful for evaluating equilibria involving metal-ligand complexes. The vertical scale of the ladder diagram represents the concentration of unreacted or free ligand, pL. The horizontal lines on the scale depict the log of stepwise formation constants for metal-ligand complexes and indicate the dominant species in all the regions.
The formation constant, K1, for the formation of Cd(NH3)2+ complex from cadmium and ammonia is 3.55 × 102. Log K1 (i.e. pNH3) is 2.55, and...

You might also read

Related Articles

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

Sort by

Same author

More connections, higher polarization: The role of weak ties and status-driven dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Biswas-Chatterjee-Sen kinetic exchange opinion model for two connected groups.

Physical review. E·2025

Same author

Coexistence of balance and hierarchies: An ego perspective to explain empirical networks.

PNAS nexus·2025

Same author

Paradise-disorder transition in structural balance dynamics on Erdös-Rényi graphs.

Physical review. E·2025

Same author

Critical properties of Heider balance on multiplex networks.

Physical review. E·2024

Same author

Protect our environment from information overload.

Nature human behaviour·2024

Search research articles

Related Experiment Video

Updated: Jun 18, 2026

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

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

Published on: December 7, 2021

Bistable-monostable transition in the Ising model on two connected complex networks.

Krzysztof Suchecki¹, Janusz A Hołyst

¹Faculty of Physics, Center of Excellence for Complex Systems Research, Warsaw University of Technology, Koszykowa 75, PL-00-662 Warsaw, Poland. ksucheck@gmail.com

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|November 13, 2009

Summary

We studied the Ising model on complex networks, finding a phase transition where magnetization jumps discontinuously. This bistable-monostable transition occurs in random and scale-free networks.

Related Experiment Videos

Last Updated: Jun 18, 2026

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

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

Published on: December 7, 2021

Area of Science:

Statistical physics
Complex networks analysis
Condensed matter theory

Background:

The Ising model is a fundamental tool for studying magnetism and phase transitions.
Complex networks offer diverse topologies that can influence system behavior.
Previous analysis explored Ising model dynamics on networks; this study extends that work.

Purpose of the Study:

To investigate the behavior of the Ising model on sparsely connected random and scale-free networks.
To analyze the occurrence and characteristics of a bistable-monostable phase transition.
To determine the critical temperature and understand magnetization dynamics.

Main Methods:

Analysis of the Ising model on random graphs and Barabási-Albert scale-free networks.
Analytical calculation of critical temperature for regular random graphs.
Utilizing an iterative map for mean-field dynamics in more general cases.
Confirmation through Monte Carlo simulations.

Main Results:

A bistable-monostable phase transition was identified in the studied network systems.
The magnetization was observed to undergo a discontinuous jump during this transition.
Analytical and simulation methods confirmed the transition and critical temperature.

Conclusions:

The Ising model exhibits a distinct phase transition on sparsely connected complex networks.
Network topology significantly influences the magnetic properties and phase transition dynamics.
The findings provide insights into critical phenomena in disordered magnetic systems.