网络上的新兴随机步行者:打击时间,预算更新和生存动态
Thomas M Michelitsch1, Alejandro P Riascos2
1Sorbonne Université, CNRS, Institut Jean Le Rond d'Alembert, F-75005 Paris, France.
Chaos (Woodbury, N.Y.)
|December 3, 2025
概括
这项研究模拟了一个凡人随机步行者,其预算在目标节点更新. 这位步行者.
科学领域:
- 统计物理 统计物理
- 网络科学 网络科学
- 可能性理论概率理论.
背景情况:
- 随机步行是各种科学学科的基本模型.
- 了解有限寿命过程的动态在金融和生物学等领域至关重要.
- 以前的模型通常假定无限寿命或更简单的重置机制.
研究的目的:
- 在网络上介绍和分析一个新的凡人随机步行者模型.
- 调查导航偏差度和目标节点更新对步行者动态的影响.
- 为了获得生存概率,寿命和击中目标的统计数据的分析结果.
主要方法:
- 开发一个离散时间的马科维亚随机步行者模型,预算正在耗尽.
- 将预算更新纳入指定的目标节点.
- 数学分析目标击中统计数据和步行者寿命,通过模拟验证.
主要成果:
- 衍生的分析表达式,用于 evanescent 传播子矩阵,生存概率和平均居住时间.
- 量化了预期的使用寿命和预算更新次数.
- 根据步行者与目标节点的互动,确定了采集者,有害和中性场景.
结论:
- 该模型为研究网络上有限寿命的随机过程提供了一个框架.
- 导航策略,目标节点频率和预算更新之间的相互作用显著影响着步行者动态.
- 这些发现提供了对资源被间歇消耗和补充的系统的洞察力.
更多相关视频
10:44Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
Published on: December 7, 2021
2.6K
05:30Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
1.1K
相关概念视频
Entropy Change in Reversible Processes
3.2K
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.
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.
3.2K
Random Variables
17.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
17.2K
Mean free path and Mean free time
4.9K
Consider the gas molecules in a cylinder. They move in a random motion as they collide with each other and change speed and direction. The average of all the path lengths between collisions is known as the "mean free path."
4.9K
Parametric Survival Analysis: Weibull and Exponential Methods
990
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
990
Current Growth And Decay In RL Circuits
4.5K
The current growth and decay in RL circuits can be understood by considering a series RL circuit consisting of a resistor, an inductor, a constant source of emf, and two switches. When the first switch is closed, the circuit is equivalent to a single-loop circuit consisting of a resistor and an inductor connected to a source of emf. In this case, the source of emf produces a current in the circuit. If there were no self-inductance in the circuit, the current would rise immediately to a steady...
4.5K
Randomized Experiments
8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
8.8K
