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

31.6K
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...
31.6K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

21.5K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
21.5K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.3K
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...
3.3K
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

19.7K
A pure, perfectly crystalline solid possessing no kinetic energy (that is, at a temperature of absolute zero, 0 K) may be described by a single microstate, as its purity, perfect crystallinity,and complete lack of motion means there is but one possible location for each identical atom or molecule comprising the crystal (W = 1). According to the Boltzmann equation, the entropy of this system is zero.
19.7K
Entropy and Solvation02:05

Entropy and Solvation

7.3K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
7.3K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.8K
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.
2.8K

You might also read

Related Articles

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

Sort by
Same author

Nuclear ubiquitin-conjugating enzyme TrUbc4 and F-box protein TrFwd1-mediated modification of Cre1 in Trichoderma reesei establishes a regulatory mechanism for carbon catabolite repression.

PLoS genetics·2026
Same author

Clinical characteristics and temporal trends of meropenem and piperacillin/tazobactam heteroresistance in Pseudomonas aeruginosa isolates.

Annals of clinical microbiology and antimicrobials·2026
Same author

Accurate and in situ monitoring of ammonia nitrogen in high-salinity waters by a halophilic Halomonas biofilm-powered biosensor.

Environmental research·2026
Same author

Effect of Polyether Ether Ketone Melt Fluidity on Crystallization Behavior of Carbon Fiber Reinforced Polyether Ether Ketone Composites.

Molecules (Basel, Switzerland)·2026
Same author

ST-GICM: A Spatiotemporal Graph Learning Framework with Intrinsic Curiosity for Robust Autonomous Exploration.

Sensors (Basel, Switzerland)·2026
Same author

Sarcopenia-associated CD8<sup>+</sup> T-cell reconstitution predicts poor outcomes in severe aplastic anemia after hematopoietic stem cell transplantation.

Annals of hematology·2026
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 26, 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

33.9K

A Super Fast Algorithm for Estimating Sample Entropy.

Weifeng Liu1, Ying Jiang1, Yuesheng Xu2

  • 1Guangdong Province Key Laboratory of Computational Science, School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

A new Monte Carlo-based algorithm significantly speeds up sample entropy calculation for complex time series analysis. This method offers substantial computational gains, independent of data length, for physiological and other signals.

Keywords:
Monte Carlo methodentropyfast algorithmsample entropy

More Related Videos

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.2K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

936

Related Experiment Videos

Last Updated: Sep 26, 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

33.9K
Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.2K
Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method
09:12

Optimization of Processing of Tiebangchui with Highland Barley Wine Based on the Box-Behnken Design Combined with the Entropy Method

Published on: May 19, 2023

936

Area of Science:

  • Complexity Science
  • Signal Processing
  • Biomedical Engineering

Background:

  • Sample entropy quantifies time series complexity, crucial for analyzing physiological signals like ECG and EEG.
  • Existing algorithms for sample entropy computation are computationally intensive, especially for large datasets (N).
  • Previous acceleration methods (e.g., kd-tree, assisted sliding box) still face limitations with large N.

Purpose of the Study:

  • To develop a significantly faster algorithm for estimating sample entropy.
  • To create a method with computational costs independent of the time series length (N).
  • To ensure the new algorithm converges to the true sample entropy with increasing trials.

Main Methods:

  • Proposed a novel, super-fast sample entropy estimation algorithm utilizing Monte Carlo principles.
  • Algorithm's computational complexity is independent of the length (N) of the time series.
  • Established the convergence rate of the Monte Carlo estimation towards the exact sample entropy.

Main Results:

  • Achieved 100-1000 times speedup compared to state-of-the-art algorithms (kd-tree, assisted sliding box).
  • Demonstrated satisfactory approximation accuracy across diverse datasets: ECG, EEG, cardiac, MVS, MD, and 1/f noise.
  • Validated the algorithm's efficiency and reliability on various signal types.

Conclusions:

  • The proposed Monte Carlo-based algorithm provides a highly efficient and accurate method for sample entropy estimation.
  • This approach overcomes the computational bottlenecks of traditional methods, enabling analysis of larger and more complex time series.
  • Offers a practical solution for real-time or large-scale analysis of physiological and other complex data.