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

Third Law of Thermodynamics02:38

Third Law of Thermodynamics

19.0K
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.
19.0K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.9K
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...
2.9K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.4K
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...
5.4K
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

24.0K
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...
24.0K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.6K
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.
2.6K
Entropy within the Cell01:22

Entropy within the Cell

10.8K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
10.8K

You might also read

Related Articles

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

Sort by
Same author

Generalized Species Richness Indices for Diversity.

Entropy (Basel, Switzerland)·2023
Same author

Entropy-Based Statistics and Their Applications.

Entropy (Basel, Switzerland)·2023
Same author

Normal Laws for Two Entropy Estimators on Infinite Alphabets.

Entropy (Basel, Switzerland)·2020
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jul 21, 2025

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

9.6K

Several Basic Elements of Entropic Statistics.

Zhiyi Zhang1

  • 1Department of Mathematics and Statistics, UNC Charlotte, Charlotte, NC 28223, USA.

Entropy (Basel, Switzerland)
|July 29, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces entropic statistics, moving beyond traditional random variables to analyze general random elements on alphabets. It establishes a theoretical framework for entropy-based statistical methods, enhancing data science inference.

Keywords:
entropic statisticsentropic-moment-generating functionentropiesentropy estimation

More Related Videos

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.1K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.6K

Related Experiment Videos

Last Updated: Jul 21, 2025

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
09:41

Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides

Published on: May 29, 2018

9.6K
Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes
09:42

Unraveling Entropic Rate Acceleration Induced by Solvent Dynamics in Membrane Enzymes

Published on: January 16, 2016

9.1K
An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

8.6K

Area of Science:

  • Statistics
  • Data Science
  • Information Theory

Background:

  • Modern data science is shifting statistical inference from traditional random variables to more general random elements residing on alphabets.
  • Existing statistical theory is well-developed for random variables but less systematic for inferences on alphabets, particularly entropy estimation.
  • The absence of familiar concepts like neighborhood and moments for random elements necessitates a new theoretical foundation for entropy-based statistics.

Purpose of the Study:

  • To introduce and discuss fundamental elements of entropic statistics.
  • To develop a rigorous theoretical framework for statistical inferences based on general random elements and entropies.
  • To extend existing statistical theorems to the entropic domain.

Main Methods:

  • Introduction of general entropies, entropic sample spaces, entropic distributions, and entropic statistics.
  • Definition of an entropic-moment-generating function.
  • Establishment of an entropic version of the Glivenko-Cantelli convergence theorem.

Main Results:

  • Several basic objects of entropic statistics are defined and discussed.
  • The entropic-moment-generating function is shown to uniquely characterize distributions from an entropic perspective.
  • An entropic Glivenko-Cantelli theorem is established, providing a convergence guarantee.

Conclusions:

  • The study lays the groundwork for a sound theoretical framework for entropic statistics.
  • The developed entropic tools and theorems enable more rigorous statistical exercises involving general random elements and entropies.
  • This work bridges the gap between traditional statistics and information-theoretic approaches in data science.