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Divergence and Curl01:15

Divergence and Curl

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The divergence of a vector field at a point is the net outward flow of the flux out of a small volume through a closed surface enclosing the volume, as the volume tends to zero. More practically, divergence measures how much a vector field spreads out or diverges from a given point. For an outgoing flux, conventionally, the divergence is positive. The diverging point is often called the "source" of the field. Meanwhile, the negative divergence of a vector field at a point means that the vector...
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Divergence and Stokes' Theorems01:06

Divergence and Stokes' Theorems

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The divergence and Stokes' theorems are a variation of Green's theorem in a higher dimension. They are also a generalization of the fundamental theorem of calculus. The divergence theorem and Stokes' theorem are in a way similar to each other; The divergence theorem relates to the dot product of a vector, while Stokes' theorem relates to the curl of a vector. Many applications in physics and engineering make use of the divergence and Stokes' theorems, enabling us to write...
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Gene Duplication and Divergence02:37

Gene Duplication and Divergence

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The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are...
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Bacterial Transformation01:33

Bacterial Transformation

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In 1928, bacteriologist Frederick Griffith worked on a vaccine for pneumonia, which is caused by Streptococcus pneumoniae bacteria. Griffith studied two pneumonia strains in mice: one pathogenic and one non-pathogenic. Only the pathogenic strain killed host mice.
Griffith made an unexpected discovery when he killed the pathogenic strain and mixed its remains with the live, non-pathogenic strain. Not only did the mixture kill host mice, but it also contained living pathogenic bacteria that...
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Trial and Error and Algorithm01:12

Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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Divergence and Curl of Electric Field01:25

Divergence and Curl of Electric Field

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The divergence of a vector is a measure of how much the vector spreads out (diverges) from a point. For example, an electric field vector diverges from the positive charge and converges at the negative charge. The divergence of an electric field is derived using Gauss's law and is equal to the charge density divided by the permittivity of space. Mathematically, it is expressed as
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相关实验视频

Updated: Jan 29, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

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反向云转换算法基于库尔巴克·莱布勒分歧.

Xiaobin Xu1, Kangwei Yu2, Junhe Fu3

  • 1China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing, Hangzhou Dianzi University, Hangzhou, China.

PloS one
|January 27, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的逆向云转换 (BCT) 算法,使用Kullback Leibler (KL) 分歧来提高云模型 (CM) 参数的准确性. 该方法通过分析数据分布来完善预期,和超估计,优于传统算法.

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Last Updated: Jan 29, 2026

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Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 云模型 (CM) 是对不确定性的双向认知工具,用于故障诊断和系统建模.
  • 逆向云转换 (BCT) 算法从定量数据中提取CM参数 (Ex,En,He).
  • 现有的BCT方法忽视了数据分布对参数准确性的影响.

研究的目的:

  • 提出一种新的BCT算法,利用Kullback Leibler (KL) 分歧进行增强的CM参数估计.
  • 通过考虑数据分布特征,解决传统BCT中集成建模的局限性.
  • 改进云模型的预期 (Ex), (En) 和超 (He) 的准确性.

主要方法:

  • 开发了一个结合KL分歧的BCT算法来分析样本数据分布.
  • 引入了一个来自粗粒度数据分析的原子化模板数据集 (ATD).
  • 实施了基于KL原子化状态差异评估的差异化BCT策略.

主要成果:

  • 拟议的KL基于分歧的BCT算法在获得CM关键参数方面表现出卓越的准确性.
  • 使用UCI基准和真实故障诊断数据进行的比较分析验证了该方法的有效性.
  • 该方法通过考虑变化的数据分布,成功地改进了参数估计.

结论:

  • 基于KL分歧的新型BCT算法为云模型参数提取提供了更准确的方法.
  • 这种方法提高了CM应用在诸如故障诊断和系统建模等领域的可靠性.
  • 考虑数据分布特征对于改进逆向云转换算法至关重要.