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Entropy02:39

Entropy

33.7K
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...
33.7K
Entropy01:18

Entropy

3.3K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.3K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.0K
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...
4.0K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.0K
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.
3.0K
Entropy and Solvation02:05

Entropy and Solvation

7.9K
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...
7.9K
Third Law of Thermodynamics02:38

Third Law of Thermodynamics

21.0K
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.
21.0K

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Related Experiment Video

Updated: Nov 27, 2025

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

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Permutation Entropy Based on Non-Uniform Embedding.

Mei Tao1, Kristina Poskuviene2, Nizar Faisal Alkayem1

  • 1Department of Engineering Mechanics, Hohai University, Nanjing 210098, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to visualize permutation entropy using attractor embedding. The technique generates a single image representing complex time series data, aiding analysis.

Keywords:
attractor embeddingmulti-dimensional phase spaceordinal patternpermutation entropy

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Area of Science:

  • Complexity Science
  • Time Series Analysis
  • Information Theory

Background:

  • Permutation entropy is a valuable measure for quantifying complexity in time series data.
  • Existing visualization methods for permutation entropy can be complex and difficult to interpret.
  • There is a need for intuitive and effective visualization techniques for time series analysis.

Purpose of the Study:

  • To present a novel visualization scheme for permutation entropy.
  • To develop a method that generates a single, interpretable image representing permutation entropy.
  • To demonstrate the advantages of the proposed scheme for analyzing time series.

Main Methods:

  • The proposed scheme utilizes non-uniform attractor embedding of time series.
  • It involves averaging all possible plain projections of the permutation entropy measure.
  • This is performed within a multi-dimensional delay coordinate space.

Main Results:

  • A single digital image effectively visualizes permutation entropy.
  • The method successfully represents complex patterns in time series data.
  • Computational experiments validated the scheme's effectiveness.

Conclusions:

  • The novel visualization scheme offers an intuitive way to analyze permutation entropy.
  • This approach enhances the understanding of time series complexity.
  • The method is applicable to both synthetic and real-world data.