Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
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.
2.5K
Expected Value01:15

Expected Value

3.9K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
3.9K
First Law: Particles in Two-dimensional Equilibrium01:18

First Law: Particles in Two-dimensional Equilibrium

5.1K
Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
Newton's first law tells us about...
5.1K
Gradient and Del Operator01:14

Gradient and Del Operator

2.6K
In mathematics and physics, the gradient and del operator are fundamental concepts used to describe the behavior of functions and fields in space. The gradient is a mathematical operator that gives both the magnitude and direction of the maximum spatial rate of change. Consider a person standing on a mountain. The slope of the mountain at any given point is not defined unless it is quantified in a particular direction. For this reason, a "directional derivative" is defined, which is a vector...
2.6K
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

318
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
318
First Law: Particles in One-dimensional Equilibrium01:10

First Law: Particles in One-dimensional Equilibrium

6.9K
Newton's first law of motion states that a body at rest remains at rest, or if in motion, remains in motion at constant velocity, unless acted on by a net external force. It also states that there must be a cause for any change in velocity (a change in either magnitude or direction) to occur. This cause is a net external force. For example, consider what happens to an object sliding along a rough horizontal surface. The object quickly grinds to a halt, due to the net force of friction. If...
6.9K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Byzantine robust federated learning for heterogeneous brain MRI using multisignal gradient fingerprinting and adaptive trust aggregation.

Scientific reports·2026
Same author

Inactivation of tumor suppressor genes and cancer therapy: An evolutionary game theory approach.

Mathematical biosciences·2017
Same author

Effects of selection and mutation on epidemiology of X-linked genetic diseases.

Mathematical biosciences and engineering : MBE·2017
Same author

Selection and mutation in X-linked recessive diseases epidemiological model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2016

相关实验视频

Updated: Jun 28, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.3K

连续空间中的网络聚合马尔科夫游戏的预期政策梯度

Alireza Ramezani Moghaddam, Hamed Kebriaei

    IEEE transactions on neural networks and learning systems
    |April 22, 2024
    PubMed
    概括

    这项研究介绍了复杂游戏中的代理人新的多代理强化学习算法. 该算法帮助代理商找到最好的策略,称为纳什平衡,即使没有完整的环境知识.

    科学领域:

    • 游戏理论 游戏理论
    • 人工智能的人工智能
    • 网络工程 网络工程

    背景情况:

    • 在无限网络聚合马尔科夫游戏中调查纳什寻找问题.
    • 专注于一个非合作的框架,其中代理人最大限度地获得奖励,而没有完整的环境或奖励功能知识.

    研究的目的:

    • 在无限动态游戏中开发一个连续的多代理强化学习 (MARL) 算法,用于纳什寻找问题.
    • 为拟议的算法提供趋同保证.

    主要方法:

    • 提出一个使用预期政策梯度 (EPG) 的演员-关键 MARL 算法.
    • 用两个一般函数近似器来估计价值函数和纳什策略.
    • 解决连续状态和动作空间,并使用新的EPG来减少梯度近似差异.

    主要成果:

    • 证明代理政策的趋同到一个独特的纳什平衡 (NE) 在传统假设,如线性函数近似器.
    • 进行估计误差分析,以了解函数近似误差的影响.

    结论:

    • 开发的MARL算法在复杂的非合作游戏中有效地引导代理人达到纳什平衡.
    • 通过云无线电接入网络 (C-RAN) 的案例研究来证明框架的适用性.

    更多相关视频

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.1K

    相关实验视频

    Last Updated: Jun 28, 2025

    The HoneyComb Paradigm for Research on Collective Human Behavior
    06:48

    The HoneyComb Paradigm for Research on Collective Human Behavior

    Published on: January 19, 2019

    9.3K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K
    Modeling the Functional Network for Spatial Navigation in the Human Brain
    05:55

    Modeling the Functional Network for Spatial Navigation in the Human Brain

    Published on: October 13, 2023

    1.1K