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相关概念视频

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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Path Between Thermodynamics States01:21

Path Between Thermodynamics States

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Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
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Neural Circuits01:25

Neural Circuits

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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

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Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
996
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

877
Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
The pressure difference depends on the fluid's velocity and radius of curvature. The pressure variation is minimal in flows with nearly straight streamlines.
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Propagation of Action Potentials01:23

Propagation of Action Potentials

5.9K
The propagation of an action potential refers to the process by which a nerve impulse, or "action potential," travels along a neuron.
Neurons (nerve cells) have a resting membrane potential, with a slightly negative charge inside compared to outside. This is maintained by ion channels, such as sodium (Na+) and potassium (K+) channels, which control the flow of ions. When a stimulus, like a touch or a signal from another neuron, triggers the neuron, sodium channels open, allowing sodium ions to...
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相关实验视频

Updated: Jul 13, 2025

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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贝雷津斯基 - 科斯特利茨 - 托勒斯从神经网络流转到神经网络流.

Kwai-Kong Ng1, Ching-Yu Huang1, Feng-Li Lin2

  • 1Department of Applied Physics, Tunghai University, Taichung 40704, Taiwan.

Physical review. E
|October 18, 2023
PubMed
概括

我们介绍了一个神经网络 (NN) 流程方法来识别2D时钟模型中的Berezinskii-Kosterlitz-Thouless (BKT) 阶段过渡. 这种方法使用Jensen-Shannon分歧温度计来检测自旋配置的临界温度.

科学领域:

  • 统计物理学的统计物理.
  • 计算物理学的计算物理.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 贝雷津斯基-科斯特利茨-托勒斯 (BKT) 阶段过渡是2D系统中的一个关键现象.
  • 了解相变对于凝聚物质物理学来说至关重要.
  • 识别相位转换的传统方法可能是计算密集的.

研究的目的:

  • 开发一种用于检测BKT相变的新方法.
  • 应用神经网络 (NN) 流和詹森-香农分歧 (JSD) 来进行相位过渡分析.
  • 为了研究具有q≥4.4的二维q状态时钟模型.

主要方法:

  • 使用神经网络 (NN) 流,包括序列变异自编码器单元.
  • 在蒙特卡洛配置上使用无监督学习训练NN流.
  • 使用Jensen-Shannon分歧 (JSD) 作为信息距离测量方法来比较状态集.
  • 分析平均旋转值的概率分布函数.

主要成果:

  • 实际上,NN流将任意的旋转状态映射到一个固定点集合.
  • JSD温度计揭示了与不同温度相应的独特配置文件.
  • 这些独特的JSD配置文件准确地识别了BKT相变的临界温度.

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  • 该方法证明了对2D q状态时钟模型 (q≥4) 的稳定性.
  • 结论:

    • 结合JSD的NN流程方法,为识别BKT相位过渡提供了一个强大的工具.
    • 这种方法提供了一个数据驱动的,可能比传统方法更有效的替代方案.
    • 这些发现有助于更深入地了解统计物理模型中的相位过渡.