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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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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...
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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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Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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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.
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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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Gibbs Free Energy02:39

Gibbs Free Energy

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One of the challenges of using the second law of thermodynamics to determine if a process is spontaneous is that it requires measurements of the entropy change for the system and the entropy change for the surroundings. An alternative approach involving a new thermodynamic property defined in terms of system properties only was introduced in the late nineteenth century by American mathematician Josiah Willard Gibbs. This new property is called the Gibbs free energy (G) (or simply the free...
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Updated: Jan 8, 2026

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
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Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

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Entropía de transferencia para datos finitos

Alec Kirkley1

  • 1University of Hong Kong, University of Hong Kong, University of Hong Kong, Institute of Data Science, Hong Kong SAR, China; Department of Urban Planning and Design, Hong Kong SAR, China; and Urban Systems Institute, Hong Kong SAR, China.

Physical review. E
|December 23, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta una nueva medida de entropía de transferencia para datos discretos, superando problemas de sesgo y significancia en el análisis de series temporales pequeñas o de alta cardinalidad. Permite una evaluación fiable del flujo de información sin simulaciones.

Palabras clave:
entropía de transferenciaseries temporales discretasanálisis de sistemas complejosteoría de la informaciónanálisis de series temporalessesgo de estimaciónprueba de significancia estadística

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Área de la Ciencia:

  • Análisis de sistemas complejos
  • Teoría de la información
  • Análisis de series temporales

Sus antecedentes:

  • La entropía de transferencia cuantifica el flujo de información dirigido, pero enfrenta desafíos con datos continuos.
  • Para datos discretos, sufre de sesgo positivo con recuentos dispersos y carece de evaluación de significancia estadística.
  • Los métodos existentes luchan con flujos de datos finitos de tamaño pequeño o alta cardinalidad.

Objetivo del estudio:

  • Desarrollar una nueva medida de entropía de transferencia para flujos de datos discretos finitos.
  • Abordar las limitaciones de los estimadores existentes, específicamente el sesgo y la falta de prueba de significancia.
  • Permitir la evaluación de significancia estadística no paramétrica sin depender de simulaciones.

Principales métodos:

  • Se derivó una nueva medida de entropía de transferencia calculando el contenido de información en flujos de datos finitos.
  • Se evitó la consideración explícita de símbolos como variables aleatorias.
  • Se aseguró la equivalencia asintótica al estimador estándar de "plug-in".

Principales resultados:

  • La nueva medida es asintóticamente equivalente al estimador estándar de "plug-in".
  • Remedia eficazmente los problemas de sesgo positivo asociados con los recuentos de bin dispersos.
  • Permite una evaluación totalmente no paramétrica de la significancia estadística para series temporales finitas.
  • El método es adecuado para series temporales de tamaño pequeño y/o alta cardinalidad.

Conclusiones:

  • La medida de entropía de transferencia propuesta ofrece una solución robusta para analizar el flujo de información en datos discretos finitos.
  • Supera las limitaciones críticas de los métodos tradicionales, mejorando la fiabilidad y la interpretabilidad.
  • Permite la validación estadística rigurosa de la transferencia de información dirigida en sistemas complejos.