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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

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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...
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Random Error01:04

Random Error

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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...
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Spearman's Rank Correlation Test01:20

Spearman's Rank Correlation Test

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
Spearman's test calculates correlation by...
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Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

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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...
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The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

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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...
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Related Experiment Video

Updated: Dec 8, 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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Correlation function inadequacy in random-sequence entropy measures.

O V Usatenko1, S S Melnyk1, G M Pritula1

  • 1O. Ya. Usikov Institute for Radiophysics and Electronics of the National Academy of Sciences of Ukraine, 12 Proskura Street, 61805 Kharkiv, Ukraine.

Physical Review. E
|September 18, 2020
PubMed
Summary
This summary is machine-generated.

This study links correlation functions and conditional entropies for random sequences using additive Markov chains. It reveals that approximating entropy with two-point distributions is accurate only for specific sequences.

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Area of Science:

  • Information Theory
  • Statistical Mechanics
  • Stochastic Processes

Background:

  • Understanding the relationship between statistical properties and information-theoretic measures in random sequences is crucial.
  • Additive Markov chains provide a framework for modeling sequential data with dependencies.

Purpose of the Study:

  • To establish a connection between correlation functions and conditional entropies for symbolic and numerical random sequences.
  • To evaluate the accuracy of using two-point probability distributions for entropy approximation in random chains.

Main Methods:

  • Utilizing the additive Markov chain approach.
  • Expressing entropy via two-point probability distribution functions.
  • Relating conditional entropy to correlation functions and Kullback-Leibler mutual information.

Main Results:

  • A relationship between correlation functions and conditional entropies was established for additive Markov chains.
  • Entropy approximation using two-point distributions is satisfactory only for specific random sequences.
  • The conditional entropy derived via two-point distributions is generally lower than the actual value for numerical sequences.

Conclusions:

  • The study highlights the limitations of the two-point distribution approximation for entropy estimation in general random sequences.
  • Conditional entropy for additive Markov chains can be decomposed into Kullback-Leibler mutual information.
  • An example demonstrates sequences with zero correlation functions but non-zero correlations.