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Related Concept Videos

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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Third Law of Thermodynamics02:38

Third Law of Thermodynamics

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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.
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Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

3.0K
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...
3.0K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.5K
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...
5.5K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.7K
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.
2.7K
Entropy and Solvation02:05

Entropy and Solvation

7.1K
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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Updated: Aug 23, 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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The maximum entropy principle for compositional data.

Corey Weistuch1, Jiening Zhu2, Joseph O Deasy1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.

BMC Bioinformatics
|October 30, 2022
PubMed
Summary
This summary is machine-generated.

Compositional Maximum Entropy (CME) models compositional systems, inferring interactions from complex data. This data-driven tool offers biologically-intuitive insights for analyzing biological and chemical systems.

Keywords:
Compositional dataInferenceMaximum entropyNetworks

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Area of Science:

  • Systems biology
  • Bioinformatics
  • Computational chemistry

Background:

  • Compositional systems, where parts form a whole, are prevalent in biology and chemistry.
  • Understanding these systems requires analyzing complex, stochastic component behaviors and data on a simplex.

Purpose of the Study:

  • To develop a data-driven modeling tool for compositional systems.
  • To infer multivariate relationships between system components.

Main Methods:

  • Compositional Maximum Entropy (CME) integrates prior geometric structure with sample-specific data.
  • CME infers underlying multivariate relationships within compositional systems.

Main Results:

  • CME was applied to bacterial abundance data to infer inter-species interactions.
  • The method demonstrated superior performance over alternatives in extracting gene-gene interactions from triple-negative breast cancer data.

Conclusions:

  • Compositional Maximum Entropy (CME) offers novel, biologically-intuitive insights.
  • CME shows promise as a quantitative framework for analyzing compositional data.