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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Pore Size Distribution01:23

Pore Size Distribution

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In concrete, the pore size distribution significantly influences the material's properties. Capillary pores, markedly larger than gel pores, form a vast network within partially hydrated cement paste, reducing the concrete's strength and increasing its permeability. This heightened permeability leads to a greater risk of damage from environmental factors like freeze-thaw cycles and chemical attacks, with the extent of vulnerability also being tied to the water-to-cement ratio.
Adequate...
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Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

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According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
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Poisson Probability Distribution01:09

Poisson Probability Distribution

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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
7.8K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

1.7K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
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Survival Tree01:19

Survival Tree

55
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

Updated: May 29, 2025

Observation and Analysis of Blinking Surface-enhanced Raman Scattering
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Observation and Analysis of Blinking Surface-enhanced Raman Scattering

Published on: January 11, 2018

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透集群上的不对称的简单排除过程:等待时间分布在侧边分支.

Chandrashekar Iyer1, Mustansir Barma1, Hunnervir Singh2

  • 1Tata Institute of Fundamental Research, 36/P, Gopanpally, Hyderabad 500046, India.

Physical review letters
|February 6, 2025
PubMed
概括

我们使用不对称的简单排除过程 (ASEP) 研究了无序介质上的粒子传输. 侧枝的长时间等待会导致动态异质性,颗粒的移动性不同.

科学领域:

  • 统计力学 统计力学
  • 复杂的系统复杂的系统.

背景情况:

  • 非对称的简单排除过程 (ASEP) 模型在无序介质中相互作用的粒子.
  • 了解像透集群这样的复杂结构上的粒子动力学至关重要.

研究的目的:

  • 在ASEP下调查透集群的侧枝颗粒的等待时间分布.
  • 分析偏差场对粒子等待时间和系统动态的影响.

主要方法:

  • 准确的等待时间分布在单侧分支的数值评估.
  • 将结果扩展到常规的结构和透集群.
  • 分析边分支中粒子的稳定状态分数.

主要成果:

  • 对于大的偏差场,日志 (等待时间) 分布会呈现多个峰值.
  • 在时间T_w尺度上的侧枝中的粒子的分数为exp(-c*sqrt(log T_w)).
  • 观察到的长时间尺度并不反映在马尔科夫进化矩阵谱中.

结论:

  • 透集群上的ASEP显示了动态异质性.
  • 粒子因复杂的等待时间分布而分为高和低流动性的区域.

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  • 该模型为无序系统中的运输现象提供了洞察力.