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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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Entropy and the Second Law of Thermodynamics01:20

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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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Standard Entropy Change for a Reaction03:00

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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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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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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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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Low-Latency Realism Through Randomized Distributed Function Computations: A Shannon Theoretic Approach.

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Related Experiment Video

Updated: Aug 9, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Towards More Efficient Rényi Entropy Estimation.

Maciej Skorski1

  • 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warszawa, Poland.

Entropy (Basel, Switzerland)
|February 25, 2023
PubMed
Summary
This summary is machine-generated.

This study enhances Rényi entropy estimation using a novel analysis of the generalized birthday paradox collision estimator. Improved bounds lead to adaptive techniques outperforming prior methods, especially for low entropy.

Keywords:
Rényi entropyadaptive estimationbirthday paradoxcollision estimation

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Area of Science:

  • Information Theory
  • Statistical Inference
  • Machine Learning

Background:

  • Rényi entropy estimation is crucial for cryptography, statistical inference, and machine learning.
  • Existing estimators face limitations regarding sample size, adaptiveness, and analytical simplicity.

Purpose of the Study:

  • To improve Rényi entropy estimators by enhancing sample size efficiency, adaptiveness, and analytical simplicity.
  • To introduce a novel, simpler analysis of the generalized birthday paradox collision estimator.
  • To develop and evaluate an adaptive estimation technique based on improved theoretical bounds.

Main Methods:

  • A simplified analysis of the generalized birthday paradox collision estimator.
  • Derivation of strengthened theoretical bounds for the estimator.
  • Development of an adaptive estimation technique leveraging the new bounds.

Main Results:

  • The novel analysis provides clearer formulas and stronger bounds compared to prior work.
  • The adaptive estimation technique demonstrates superior performance, particularly in low to moderate entropy scenarios.
  • The study discusses broader applications of the developed techniques for birthday estimators.

Conclusions:

  • The enhanced analysis and adaptive technique offer significant improvements for Rényi entropy estimation.
  • The findings are relevant for various fields, including cryptography, statistical inference, and machine learning.
  • The work provides a foundation for further research into birthday estimators and their applications.