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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
Entropy02:39

Entropy

30.2K
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...
30.2K
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

5.3K
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...
5.3K
Entropy and the Second Law of Thermodynamics01:20

Entropy and the Second Law of Thermodynamics

2.8K
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...
2.8K
Entropy and Solvation02:05

Entropy and Solvation

7.1K
The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
7.1K
Entropy within the Cell01:22

Entropy within the Cell

10.7K
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...
10.7K

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相关实验视频

Updated: Jul 10, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.4K

探索隐藏互动的透复杂网络.

Alex Arturo Centeno Mejia1, Moisés Felipe Bravo Gaete2

  • 1Doctorado en Modelamiento Matemático Aplicado, Universidad Católica del Maule, Avenida San Miguel, Talca 3605, Chile.

Entropy (Basel, Switzerland)
|November 24, 2023
PubMed
概括

我们引入了一个潜在的交互指数来分析复杂的网络,通过考虑未观察到的节点特征来改进传统方法. 这种方法增强了对网络动态的理解,并在微生物社区检测中有应用.

科学领域:

  • 网络科学 网络科学
  • 统计建模 统计建模
  • 复杂系统分析 复杂系统分析

背景情况:

  • 由于未观察到的节点异质性,传统的组合相似性指数与大型复杂网络作斗争.
  • 现有的方法在捕捉复杂的网络结构中的微妙相互作用方面存在局限性.

研究的目的:

  • 为复杂网络分析引入一种新的潜在交互指数.
  • 开发一个Shannon类型的值函数来表征网络密度和拓.
  • 应用这些方法来分析组成结构动态和检测微生物群落.

主要方法:

  • 开发一种隐性相互作用指数,包括观察到的和未观察到的节点异质性.
  • 制定一个Shannon类型的值函数来评估网络密度并建立最佳边界.
  • 利用网络拓学来更好地估计和分析非对称性质.
  • 模拟将拟议的方法与埃尔多斯-雷尼和巴拉巴西-阿尔伯网络模型进行比较.

主要成果:

  • 隐性交互指数有效地捕获节点特定信息,并减轻大型网络中的估计问题.
  • 香农型值函数提供了网络密度的可靠度量,并确定了最佳边界.
  • 分析揭示了对构成结构动态和网络内的复杂相互作用的洞察力.
关键词:
复杂的网络复杂的网络.进入的过程中,估计估计估计的估计.隐藏的相互作用指数.

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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相关实验视频

Last Updated: Jul 10, 2025

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Published on: August 21, 2019

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  • 模型在检测微生物群落中的成功应用.
  • 结论:

    • 拟议的潜相互作用指数和函数为理解复杂的网络结构和动态提供了一个强大的框架.
    • 这种方法克服了传统指数的局限性,为异质性提供了更准确的分析.
    • 该方法显示了广泛的适用性,包括微生物社区检测的重大挑战.