Jove
Visualize
联系我们

相关概念视频

Central Limit Theorem01:14

Central Limit Theorem

15.2K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
15.2K
What is Central Tendency?01:14

What is Central Tendency?

14.9K
Descriptive statistics describe or summarize relevant characteristics of a sample and aid in the analysis of data of interest. When analyzing large quantities of data and developing an inference, one needs to identify a value representative of the entire data set. Characteristics such as central tendency, extreme values, range of measurements, or the most repeated value can help better understand the data.
The central tendency is the most conventionally used data characteristic. It is a...
14.9K
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.6K
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.
2.6K
Central Tendency: Analysis01:10

Central Tendency: Analysis

171
Measures of central tendency are tools used in biostatistics to identify the average or center of a dataset. They offer a single representative value for understanding and summarizing data distribution.
The mean is one such measure, calculated by totaling all values in a dataset and dividing by the number of values. For instance, the mean blood pressure reading (120, 130, 140, 150) would be 135. However, the mean can be affected by extreme values or outliers.
The median, another measure,...
171
Entropy within the Cell01:22

Entropy within the Cell

10.7K
A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
10.7K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

20.5K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
20.5K

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

A New Transformation Technique for Reducing Information Entropy: A Case Study on Greyscale Raster Images.

Entropy (Basel, Switzerland)·2023
Same author

A Review of Federated Learning in Agriculture.

Sensors (Basel, Switzerland)·2023
Same author

FLoCIC: A Few Lines of Code for Raster Image Compression.

Entropy (Basel, Switzerland)·2023
Same author

IoT and Satellite Sensor Data Integration for Assessment of Environmental Variables: A Case Study on NO<sub>2</sub>.

Sensors (Basel, Switzerland)·2022
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

相关实验视频

Updated: Jul 18, 2025

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

在复杂网络中,基于密度的透对社区检测的中心作用.

Krista Rizman Žalik1, Mitja Žalik1

  • 1Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

在复杂网络中识别必要的节点是关键. 一个新的基于密度的中心性测量有效地发现了这些重要的社区检测节点.

关键词:
社区检测 社区检测标签传播 标签传播网络 网络 网络 网络 网络 网络节点的中心性 节点的中心性不定向图形是指非定向的图形.

更多相关视频

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

相关实验视频

Last Updated: Jul 18, 2025

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
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K

科学领域:

  • 网络科学 网络科学
  • 图形理论 图形理论
  • 数据挖掘 数据挖掘

背景情况:

  • 识别基本节点是复杂网络分析中的一个关键挑战.
  • 具有重大本地作用的节点通常代表现实世界社区的中心.
  • 准确的社区检测依赖于选择合适的种子节点.

研究的目的:

  • 引入一种新的中心性测量方法,即基于密度的中心性,用于在本地识别重要的节点.
  • 在复杂网络中提高社区检测的准确性和效率.

主要方法:

  • 提出基于密度的中心性,这是基于节点及其邻近的最大集团大小的的衡量标准.
  • 适用于当地节点重要性识别和社区种子选择的措施.

主要成果:

  • 拟议的基于密度的中心性有效地识别了本地重要的节点.
  • 这项新措施在社区种子选择和检测方面优于现有的中心性措施.
  • 该方法是高效的,适用于大型和定义不良的网络.

结论:

  • 基于密度的中心性为本地节点的重要性和社区检测提供了强大的方法.
  • 它提供了一种有效和准确的方法来识别复杂网络中的社区结构.