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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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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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Randomized Experiments01:13

Randomized Experiments

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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...
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Random Variables01:09

Random Variables

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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...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125
Lattice Centering and Coordination Number02:33

Lattice Centering and Coordination Number

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The structure of a crystalline solid, whether a metal or not, is best described by considering its simplest repeating unit, which is referred to as its unit cell. The unit cell consists of lattice points that represent the locations of atoms or ions. The entire structure then consists of this unit cell repeating in three dimensions. The three different types of unit cells present in the cubic lattice are illustrated in Figure 1.
Types of Unit Cells
Imagine taking a large number of identical...
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Stability of structures01:14

Stability of structures

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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相关实验视频

Updated: Jul 19, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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使用有序随机图模型找到社区结构.

Masaki Ochi1, Tatsuro Kawamoto2

  • 1Department of Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8574, Japan.

Physical review. E
|August 16, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的算法来排序网络矩阵. 该方法改善了社区结构的可视化,比现有技术更清楚地揭示了网络组件.

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相关实验视频

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科学领域:

  • 网络科学 网络科学
  • 图形理论是指图形的理论.
  • 数据可视化数据可视化

背景情况:

  • 邻近矩阵可视化揭示了网络宏观特征.
  • 社区结构,以密集的组件为特征,以块对角形形式出现.
  • 经典的排序算法难以对准可见社区结构的矩阵.

研究的目的:

  • 为邻近矩阵开发一种新的排序算法.
  • 提高社区结构在网络中的可见性.
  • 在识别网络社区方面超越现有的排序算法.

主要方法:

  • 提出了一个基于最大概率估计的排序算法.
  • 使用有序随机图模型对矩阵元素对齐.
  • 将拟议的方法与经典的排序算法进行比较.

主要成果:

  • 拟议的算法有效地对准矩阵元素.
  • 社区结构变得更加清晰地识别.
  • 与现有方法相比,在社区结构检测方面表现优越.

结论:

  • 新的订单算法增强了网络可视化.
  • 最大概率估计为矩阵排序提供了一个有效的方法.
  • 这种方法可以更好地识别复杂网络中的社区结构.