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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.
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
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...
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...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...

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Related Experiment Video

Updated: Jun 3, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section
11:00

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

Published on: July 19, 2016

[Complex systems variability analysis using approximate entropy].

Eduardo Cuestas1

  • 1Servicio de Pediatría y Neonatología. Hospital Privado. Cátedra de Clínica Pediátrica, Argentina. ecueatas@hospitalprivadosa.com.ar

Revista De La Facultad De Ciencias Medicas (Cordoba, Argentina)
|April 1, 2011
PubMed
Summary

Biological systems exhibit complex dynamics. Analyzing physiological variability using chaos theory and nonlinear dynamics, like approximate entropy, aids in distinguishing disease states.

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Related Experiment Videos

Last Updated: Jun 3, 2026

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Area of Science:

  • Complex Systems Biology
  • Physiological Dynamics

Context:

  • Biological systems are intricate, interdependent networks where emergent properties depend on holistic integrity.
  • Disease is viewed as a systemic functional alteration, impacting the body's dynamic stability and rhythms.

Purpose:

  • To review recent advancements in measuring and characterizing biological variability.
  • To explore the application of mathematical models from chaos theory and nonlinear dynamics in analyzing biological signals.

Summary:

  • Biological systems possess complex, interconnected dynamic networks with unique properties.
  • Abnormal physiological rhythms, termed "dynamic disease," are linked to disease pathogenesis.
  • Analysis of variability in clinical parameters offers additional insights beyond absolute values.

Impact:

  • New mathematical models, such as approximate entropy, enhance the ability to differentiate between distinct patient groups based on biological signals.
  • This approach provides a more nuanced understanding of health and disease by analyzing dynamic patterns.