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

Entropy02:39

Entropy

28.6K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
28.6K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.7K
The second law of thermodynamics can be stated quantitatively using the concept of entropy. Entropy is the measure of disorder of the system.
The relation  between entropy and disorder can be illustrated with the example of the phase change of ice to water. In ice, the molecules are located at specific sites giving a solid state, whereas, in a liquid form, these molecules are much freer to move. The molecular arrangement has therefore become more randomized. Although the change in average...
2.7K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.1K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be...
5.1K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

22.8K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic...
22.8K
Random Error01:04

Random Error

798
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
798
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
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.
2.5K

You might also read

Related Articles

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

Sort by
Same author

Hysteretic Conductance in Ion Channel Gating.

Entropy (Basel, Switzerland)·2026
Same author

Ground State Energy Fluctuations of Pinned Elastic Manifolds.

Journal of statistical physics·2026
Same author

Emergent Nonthermal Fluid from Jets in the Massive Schwinger Model Using Tensor Networks.

Physical review letters·2025
Same author

Using space-filling curves and fractals to reveal spatial and temporal patterns in neuroimaging data.

Journal of neural engineering·2025
Same author

Intensity statistics inside an open wave-chaotic cavity with broken time-reversal invariance.

Physical review. E·2023
Same author

Scale-free correlations in the dynamics of a small (N∼10000) cortical network.

Physical review. E·2023
Same journal

Erratum: Spectroscopy and Ground-State Transfer of Ultracold Bosonic ^{39}K^{133}Cs Molecules [Phys. Rev. Lett. 135, 203401 (2025)].

Physical review letters·2026
Same journal

Erratum: Lifetime of the ^{2}F_{7/2} Level in Yb^{+} for Spontaneous Emission of Electric Octupole Radiation [Phys. Rev. Lett. 127, 213001 (2021)].

Physical review letters·2026
Same journal

Laser-Plasma Based Seeded Free Electron Laser in the High-Gain Regime.

Physical review letters·2026
Same journal

Parent Hamiltonians for Stabilizer Quantum Many-Body Scars.

Physical review letters·2026
Same journal

Properties of Heavy Cosmic Nuclei Phosphorus, Chlorine, Argon, Potassium, and Calcium: Results from the Alpha Magnetic Spectrometer.

Physical review letters·2026
Same journal

Role of Spin-Isospin Symmetries in Nuclear β-Decays.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: May 22, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K

Nonorthogonal Eigenvectors, Fluctuation-Dissipation Relations, and Entropy Production.

Yan V Fyodorov1, Ewa Gudowska-Nowak2, Maciej A Nowak2

  • 1King's College London, Department of Mathematics, London WC2R 2LS, United Kingdom.

Physical Review Letters
|March 14, 2025
PubMed
Summary
This summary is machine-generated.

This study extends the fluctuation-dissipation theorem (FDT) to non-normal matrices, revealing enhanced entropy production. This finding impacts neural network models, explaining phenomena like synchronization and memory emergence.

More Related Videos

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.4K
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.6K

Related Experiment Videos

Last Updated: May 22, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.6K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.4K
Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

11.6K

Area of Science:

  • Statistical Mechanics
  • Non-equilibrium Dynamics
  • Complex Systems

Background:

  • The fluctuation-dissipation theorem (FDT) is a cornerstone of equilibrium statistical mechanics, linking system response to correlations.
  • Standard FDT applies to systems with normal transition probability matrices.

Purpose of the Study:

  • To extend the FDT to systems with strictly non-normal transition probability matrices.
  • To investigate the impact of non-orthogonality on system dynamics and entropy production.

Main Methods:

  • Mathematical formulation of FDT for non-normal matrices.
  • Incorporation of eigenvector non-orthogonality using Chalker-Mehlig overlap matrices.
  • Analytical evaluation of entropy production rates for specific models (Ginibre matrix, Rajan-Abbott model).

Main Results:

  • Non-normal matrices significantly modify dynamics by introducing eigenvector non-orthogonality.
  • The rate of entropy production per unit time is strongly enhanced by non-normal matrices.
  • Analytical results for entropy production are derived for large Ginibre matrices and the Rajan-Abbott neural network model.

Conclusions:

  • The developed FDT extension provides a mechanism for enhanced entropy production in systems with non-normal dynamics.
  • This mechanism is relevant for understanding collective phenomena in neural matrix models, such as synchronization and memory.
  • The findings are generalizable to various phenomena driven by non-normal operators.