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

Structural Classification of Joints01:20

Structural Classification of Joints

3.1K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
3.1K
Structural Properties and Dimensions of Lumber01:21

Structural Properties and Dimensions of Lumber

68
Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
68
Introduction to Structures01:30

Introduction to Structures

939
A structure is defined as a system of interconnected members designed to support or transfer forces and successfully withstand the loads acting on them. The internal forces of a structure can be determined by decomposing the structure and analyzing the free-body diagrams of the individual members or of a combination of members. This helps in understanding the structural elements' behavior and ensuring that the structure is stable and can withstand the subjected loads.
There are three main...
939
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

1.2K
When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal...
1.2K
Structural Isomerism02:34

Structural Isomerism

19.0K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly,...
19.0K
Structuralism01:26

Structuralism

518
Structuralism, an early psychological theory developed by Wilhelm Wundt and his student Edward Bradford Titchener, sought to dissect the human mind into its most fundamental components. Wundt's groundbreaking work in his laboratory set the stage for Titchener to define structuralism's goal as cataloging the "atoms" of the mind—sensations, images, and feelings—akin to how chemists identify elements of matter.
Titchener's approach to structuralism was unique. He...
518

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

Dichography: two-frame ultrafast imaging from a single diffraction pattern.

Nature communications·2026
Same author

Stoichiometry-Induced Band Gap Opening in Epitaxial Degenerate Copper Sulfide Thin Films.

The journal of physical chemistry letters·2026
Same author

Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.

Journal of the American Chemical Society·2026
Same author

<i>Operando</i> XPS studies of precisely size-selected Pd nano-catalysts for methane oxidation.

Faraday discussions·2026
Same author

A Catalyst-Coated Mesoporous Carbon-Membrane Electrode Assembly for In Situ Soft X‑ray XPS and NEXAFS Studies of Electrocatalytic Interfaces.

ACS electrochemistry·2026
Same author

Tuning the Wettability of Hydrophobic Metal-Organic Frameworks by Linker-Doping.

ACS nano·2026
Same journal

Switchable band alignment in 2D-perovskite/WS<sub>2</sub>heterostructures for tunable exciton transport and valley polarization.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same journal

Chiral graviton modes in fermionic Fractional Chern Insulators.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same journal

Bound states in the continuum in plasmonic structures.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same journal

Unlocking complex optical vortices with flat optics.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same journal

Pseudo-Hermitian magnon dynamics.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same journal

Uniaxial-stress-induced magnetic transitions in the triangular-lattice antiferromagnet PdCrO<sub>2</sub>.

Reports on progress in physics. Physical Society (Great Britain)·2026
查看所有相关文章

相关实验视频

Updated: May 17, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

913

通过机器学习内在的结构描述符.

Emanuele Telari1, Antonio Tinti2, Manoj Settem3

  • 1Sapienza University of Rome, Via Eudossiana 18, Rome, 00184, ITALY.

Reports on progress in physics. Physical Society (Great Britain)
|May 15, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种机器学习 (ML) 方法,用于识别复杂模拟中的关键变量. 这种方法有助于理解系统行为,并增强了先进的模拟技术的应用.

关键词:
集体变量是指集体变量.计算自由能源的计算方法机器学习 机器学习金属纳米集群金属纳米集群分子动力学分子动力学

更多相关视频

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.0K

相关实验视频

Last Updated: May 17, 2025

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

913
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K
Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps
08:59

Morphology-Based Distinction Between Healthy and Pathological Cells Utilizing Fourier Transforms and Self-Organizing Maps

Published on: October 28, 2018

7.0K

科学领域:

  • 计算化学是一种计算化学.
  • 材料科学是一种材料科学.
  • 生物物理学的生物物理.

背景情况:

  • 识别集体变量对于解释复杂的系统模拟和应用增强的采样技术至关重要.
  • 当前的方法在从模拟数据中提取物理相关的变量时面临挑战.

研究的目的:

  • 开发一种机器学习 (ML) 方法,从系统配置中识别物理相关的集体变量.
  • 应用这种ML策略来表征纳米集群和系统中的复杂结构转变.

主要方法:

  • 一种新的机器学习 (ML) 方法将瞬间的系统配置与液体理论中固有的结构联系起来.
  • 该方法应用于147原子金纳米集群系统,以分析结构过渡.
  • 此外,ML策略还在布拉迪基宁的结构重组上进行了测试.

主要成果:

  • ML衍生的固有结构变量有效地描述了金纳米集群中的结构复杂性.
  • 这些变量使得可以计算自由能源景观和过渡率.
  • 该方法成功地描述了非平衡融和结过程以及形状变化.

结论:

  • 拟议的ML方法为发现复杂系统中必不可少的集体变量提供了一个强大的工具.
  • 这种方法增强了模拟和实验数据在各种系统 (如液体,玻璃和蛋白质) 的解释.
  • 机器学习策略证明了其广泛的适用性和促进分子模拟和增强的采样技术的潜力.