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相关概念视频

The Evidence for Evolution02:55

The Evidence for Evolution

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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Convergent Evolution01:54

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Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.
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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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Eukaryotic Evolution01:24

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The endosymbiont theory is the most widely accepted theory of eukaryotic evolution; however, its progression is still somewhat debated. According to the nucleus-first hypothesis, the ancestral prokaryote first evolved a membrane to enclose DNA and form the nucleus. Conversely, the mitochondria-first hypothesis suggests that the nucleus was formed after endosymbiosis of mitochondria.
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John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
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Updated: Feb 5, 2026

Synthesis of Bimetallic Pt/Sn-based Nanoparticles in Ionic Liquids
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使用协作差分进化进行双金属集群结构优化的C++工具包.

Xiaomin Wu1, Miao He2, Yousi Lin1

  • 1School of Optoelectronic and Communication Engineering, Xiamen University of Technology, Xiamen 361024, China.

Journal of chemical information and modeling
|February 4, 2026
PubMed
概括
此摘要是机器生成的。

我们开发了一个协作差异进化 (CDE) 算法,用于高效的纳米集群结构预测. 这种方法加速了对双金属和单金属集群的稳定配置的发现.

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相关实验视频

Last Updated: Feb 5, 2026

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科学领域:

  • 材料科学 材料科学 材料科学
  • 计算化学的计算化学
  • 纳米技术纳米技术

背景情况:

  • 双金属和单金属集群结构的全球优化是计算密集的.
  • 随着系统的大小和复杂性,可能的配置 (同质组) 的数量迅速增加.

研究的目的:

  • 开发一种高效的算法,用于预测纳米集群系统的稳定结构.
  • 解决各种集群结构在全球优化的计算挑战.

主要方法:

  • 一个协作差异进化 (CDE) 算法的介绍.
  • 利用多个子群的协作架构与专门的子群进行探索,开发和平衡.
  • 为金属纳米集群量身定制的适应性操作的实施以及作为在线C++工具包的可用性.

主要成果:

  • 通过对Pt-Pd,Cu-Au双金属和单金属Pt集群的结构优化,证明了多功能性和强度.
  • 与传统方法相比,实现了比50-100%更快的趋同.
  • 在所有测试系统中,在结构预测方面展示了卓越的稳定性.

结论:

  • CDE算法是一个强大的和可泛化的工具,用于加速在多种集群材料中发现稳定的配置.
  • 开发的工具包为纳米集群结构预测提供了高效和用户友好的解决方案.