Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

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.
The Entropy as a State Function01:14

The Entropy as a State Function

Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
Entropy02:39

Entropy

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...
Entropy01:18

Entropy

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

Entropy and the Second Law of Thermodynamics

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...
Entropy and the Second Law of Thermodynamics01:26

Entropy and the Second Law of Thermodynamics

Consider an isolated system in which a hot object is placed in contact with a cold one. This is an irreversible process that eventually leads both objects to reach the same equilibrium temperature. It is crucial to note that the constituents of any substance exhibit increased disorder at higher temperatures. As a cold substance absorbs heat, its constituents become more disordered. The energy transfer from a hotter object to a cooler one increases the system's disorder or randomness. This...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Mapping the future of medicine through digital twins.

Frontiers in molecular medicine·2026
Same author

Implementing physics-informed neural networks with deep learning for differential equations.

Frontiers in artificial intelligence·2026
Same author

Screening for Celiac Disease in Childhood: Cost-Effectiveness of Multiple Genetic and Serological Testing Approaches.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2025
Same author

The role of digital twins in P4 medicine: A paradigm for modern healthcare.

NPJ digital medicine·2025
Same author

Digital twin models for predicting venetoclax and azacitidine-induced neutropenia in patients with acute myeloid leukemia.

NPJ digital medicine·2025
Same author

Screening for Left Ventricular Hypertrophy Using Artificial Intelligence Algorithms Based on 12 Leads of the Electrocardiogram-Applicable in Clinical Practice?-Critical Literature Review with Meta-Analysis.

Healthcare (Basel, Switzerland)·2025

Related Experiment Videos

Connections between classical and parametric network entropies.

Matthias Dehmer1, Abbe Mowshowitz, Frank Emmert-Streib

  • 1Institute for Bioinformatics and Translational Research, UMIT, Hall in Tirol, Austria. matthias.dehmer@umit.at

Plos One
|January 20, 2011
PubMed
Summary

This study investigates graph complexity measures, linking classical and parametric approaches. Findings establish foundational inequalities for classifying entropy-based graph complexity metrics.

Related Experiment Videos

Area of Science:

  • Graph theory and network analysis
  • Information theory and computational complexity

Background:

  • Classical graph complexity measures rely on vertex decompositions via equivalence relations.
  • Parametric measures utilize information functions to assign vertex probabilities.

Purpose of the Study:

  • To explore the relationships between classical and parametric measures of graph complexity.
  • To lay a foundation for classifying entropy-based graph complexity measures.

Main Methods:

  • Comparison of classical graph complexity measures (vertex decomposition) with parametric measures (information functions).
  • Establishment of mathematical inequalities relating these two types of measures.

Main Results:

  • Demonstrated inequalities connecting classical and parametric graph complexity measures.
  • Provided a basis for a systematic classification of entropy-based graph complexity.

Conclusions:

  • The established inequalities are crucial for understanding and categorizing graph complexity.
  • This work advances the systematic classification of entropy-based graph complexity metrics.