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Related Concept Videos

Thermodynamic Systems01:06

Thermodynamic Systems

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A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
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Path Between Thermodynamics States01:21

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Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
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Entropy Change in Reversible Processes01:10

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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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Second Law of Thermodynamics02:49

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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. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
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Second Law of Thermodynamics00:53

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The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
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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.
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Related Experiment Video

Updated: Nov 27, 2025

Time-dependent Increase in the Network Response to the Stimulation of Neuronal Cell Cultures on Micro-electrode Arrays
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Thermodynamic Analysis of Time Evolving Networks.

Cheng Ye1, Richard C Wilson2, Luca Rossi3

  • 1Department of Computer Science, Royal Holloway, University of London, Egham TW20 0EX, UK.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary

This study introduces a thermodynamic framework to analyze complex network structures and their evolution over time. This method effectively characterizes critical events in dynamic networks using simple network statistics.

Keywords:
internal energytemperaturetime-varying complex networksvon Neumann entropy

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Area of Science:

  • Complex network analysis
  • Statistical physics
  • Data science

Background:

  • Understanding the structure and evolution of complex networks is crucial.
  • Existing methods may not fully capture dynamic network changes.
  • Time-varying network analysis requires robust characterization tools.

Purpose of the Study:

  • To propose a novel thermodynamic framework for representing and analyzing time-varying complex networks.
  • To provide a method for understanding network evolution using thermodynamic principles.
  • To demonstrate the framework's ability to identify critical events in dynamic network structures.

Main Methods:

  • Utilizing an approximation of network von Neumann entropy as thermodynamic entropy.
  • Defining an internal energy for networks.
  • Calculating the temperature between networks at consecutive time points.
  • Employing simple network statistics (size, degree) for computations.

Main Results:

  • The thermodynamic framework successfully represents time-varying network structures.
  • Thermodynamic variables are computable using basic network statistics.
  • The method effectively characterizes critical events and distinct periods in network evolution.
  • Real-world financial and biological network data validated the approach.

Conclusions:

  • The proposed thermodynamic framework offers a powerful tool for analyzing dynamic complex networks.
  • This approach enhances the understanding of network time evolution.
  • The method is computationally efficient and applicable to diverse real-world systems.