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Entropy02:39

Entropy

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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...
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Entropy01:18

Entropy

3.4K
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...
3.4K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

4.6K
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...
4.6K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.1K
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.
3.1K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

23.8K
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.
23.8K
Entropy and Solvation02:05

Entropy and Solvation

8.1K
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...
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Related Experiment Video

Updated: Dec 25, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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On the automatic parameter selection for permutation entropy.

Audun Myers1, Firas A Khasawneh1

  • 1Department of Mechanical Engineering, Michigan State University, East Lansing, Michigan 48824, USA.

Chaos (Woodbury, N.Y.)
|April 3, 2020
PubMed
Summary
This summary is machine-generated.

Accurate parameter selection for Permutation Entropy (PE) is crucial for time series analysis. This study introduces automated methods for choosing the permutation dimension (n) and embedding delay (τ), improving upon heuristic approaches.

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Area of Science:

  • Complex systems analysis
  • Time series analysis
  • Nonlinear dynamics

Background:

  • Permutation Entropy (PE) quantifies time series complexity but requires precise parameter selection (dimension n, delay τ).
  • Current methods for parameter selection rely on expert heuristics or trial-and-error, which are inefficient and potentially inaccurate.
  • Automated parameter selection is essential for reliable and efficient application of PE in diverse fields.

Purpose of the Study:

  • To develop and evaluate automated methods for selecting the permutation dimension (n) and embedding delay (τ) for Permutation Entropy (PE).
  • To compare the performance of novel and existing parameter selection techniques across various types of time series data.
  • To identify the most effective methods for specific system categories.

Main Methods:

  • Development of a frequency-domain approach using least median of squares and Fourier spectrum for τ selection.
  • Extension of Permutation Auto-Mutual Information Function and Multi-scale Permutation Entropy (MPE) for τ determination.
  • Evaluation of automated methods against expert-defined parameters for diverse systems including periodic, chaotic, and biological data.

Main Results:

  • The optimal method for selecting τ depends on the system's nature: frequency-domain approach excels for periodic systems and biological data (ECG/EEG), while mutual information is best for chaotic systems.
  • For the permutation dimension n, False Nearest Neighbors and MPE provide accurate values, with n=5 being broadly applicable.
  • Automated methods demonstrate significant improvements in accuracy and efficiency over traditional approaches.

Conclusions:

  • Automated selection of PE parameters (n and τ) is feasible and system-dependent.
  • The proposed frequency-domain and extended MPE methods offer robust alternatives to manual parameter tuning.
  • Accurate parameter selection is key to unlocking the full potential of PE in complex time series analysis.