Jove
Visualize
联系我们
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

相关概念视频

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
34

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

Rational Selection of Minimal Sensor Arrays for Analyte Fingerprinting.

Analytical chemistry·2026
Same author

Corona-dependent enhanced fluorescence response of defects-induced single-walled carbon nanotubes to organophosphate.

Nanoscale·2026
Same author

Quantifying Population Reversibility of Sensor Performance in Multi-Cycle Single-Sensor Recovery Assay.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Identification and quantification of irreversibility in stochastic systems.

Physical chemistry chemical physics : PCCP·2026
Same author

Improving Greater Caribbean manatee vocalization detection across habitats using neural networks.

PloS one·2026
Same author

Post-functionalization modification as a modular strategy for size-selective fluorescence response of single-walled carbon nanotubes to polycyclic aromatic hydrocarbons.

Materials horizons·2025

相关实验视频

Updated: May 15, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.0K

没有平衡的自组合控制通过随机景观方法.

Michael Faran1, Gili Bisker1,2,3,4,5

  • 1Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv 69978, Israel.

Journal of chemical information and modeling
|April 8, 2025
PubMed
概括

这项研究引入了一种新的反控制方法,以增强分子自我组装. 短暂的能量调制提高了组装效率和精度,克服了复杂系统的局限性.

更多相关视频

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
12:33

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

Published on: February 4, 2013

21.6K
Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles
10:23

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Published on: May 8, 2015

11.6K

相关实验视频

Last Updated: May 15, 2025

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior
10:07

Generating Controlled, Dynamic Chemical Landscapes to Study Microbial Behavior

Published on: January 31, 2020

6.0K
Origami Inspired Self-assembly of Patterned and Reconfigurable Particles
12:33

Origami Inspired Self-assembly of Patterned and Reconfigurable Particles

Published on: February 4, 2013

21.6K
Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles
10:23

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Published on: May 8, 2015

11.6K

科学领域:

  • 纳米技术 纳米技术
  • 材料科学 材料科学 材料科学
  • 生物物理学的生物物理.

背景情况:

  • 自组装对于在自然和技术中创建复杂结构至关重要.
  • 当前的方法往往缺乏纠错,限制了精度,尤其是强相互作用.

研究的目的:

  • 开发一个闭环反控制策略,以优化自组装过程.
  • 提高合成和生物系统中结构形成的效率和精度.

主要方法:

  • 作为不平衡驱动器,利用相互作用能量中的短暂调制.
  • 采用随机景观方法进行实时系统分析和控制.
  • 模仿细胞过程和随机重置原理.

主要成果:

  • 在动力捕捉条件下,在组装产量方面取得了显著的改进.
  • 在各种场景中显示了组装时间的显著减少.
  • 验证了动态能量调制对优化自组装的有效性.

结论:

  • 开发的数据驱动框架提供了一个广泛适用的方法来优化不平衡组装.
  • 这一策略在精密制造和响应性材料设计中具有潜在的应用.
  • 在合成和生物背景下对受控分子组合的理解有所进步.