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

Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

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

3D Imaging of Gene Expression Domains in the Mouse Heart.

Methods in molecular biology (Clifton, N.J.)·2026
Same author

A layer specific histological framework for synaptic quantification in hippocampal CA1.

Neuroscience research·2026
Same author

The KIF3B/B/KAP3 tail domain specifically facilitates TRIM46 transport to the axon initial segment.

The Journal of cell biology·2026
Same author

<i>Fgf10</i> Gene Dosage from a Single Allele Is Insufficient for Forming Multilayered Epithelial Cells in the Murine Lacrimal Gland.

International journal of molecular sciences·2026
Same author

Thin-Adipose Compartment at the Colonic Mesentery-Perirenal Fat Interface: Histological and Three-Dimensional Morphological Studies.

International journal of urology : official journal of the Japanese Urological Association·2026
Same author

Pedal Pull-Through Rail and Wingman-Assisted Coaxial Push for Entrapped Device Retrieval in Calcified Tibial Arteries.

JACC. Case reports·2026

相关实验视频

Updated: Jul 6, 2026

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

14.3K

使用尤卡里方法优化解剖解剖团队:基于同行兼容性的方法.

Tohru Murakami1, Toru Araki2, Yuki Tajika1,3

  • 1Department of Anatomy, Gunma University Graduate School of Medicine, Maebashi, Japan.

Anatomical sciences education
|October 3, 2025
PubMed
概括
此摘要是机器生成的。

优卡里方法优化了使用同行偏好的解剖解剖团队,提高了10%的学术表现. 这种人工智能驱动的方法提高了学生的满意度和医学教育中的学习成果.

关键词:
尤卡里方法 尤卡里方法解剖学教育的教育.组合优化的优化.剖析团队的分配 分解组的分配提高学习成果,提高学习成果.当地搜索算法局部搜索算法同行兼容性 同行兼容性基于团队的团队学习.

更多相关视频

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
06:44

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

2.9K
Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication
07:47

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

1.2K

相关实验视频

Last Updated: Jul 6, 2026

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain
11:25

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

14.3K
Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues
06:44

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

2.9K
Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication
07:47

Author Spotlight: 3D Scanning and Augmented Reality for Enhanced Cancer Surgery Communication

Published on: December 15, 2023

1.2K

科学领域:

  • 医学教育 医学教育
  • 人体解剖学 解剖学 解剖学
  • 教育技术的教育技术

背景情况:

  • 人体解剖对医学培训至关重要,需要有效的团队合作.
  • 学生在剖析中的成功取决于强大的团队动态.
  • 现有的团队分配方法可能无法优化兼容性.

研究的目的:

  • 介绍Yukari方法,一个用于优化解剖解剖团队任务的自动化系统.
  • 通过改善团队动态来增强学生的学习体验和学术成果.

主要方法:

  • 开发了一个使用启发式本地搜索算法的自动化系统 (Yukari方法).
  • 通过安全的网络调查收集学生的同行偏好和动机水平.
  • 在团队任务中最大限度地提高同行兼容性.

主要成果:

  • 与随机或自我选择的团队相比,Yukari分配的团队在学业表现上有10%的改善.
  • 学生的满意度显著高于尤卡里分配的团队,而不是随机分配.
  • 增加的满意度与改善的学业成绩有积极的相关性.

结论:

  • 尤卡里方法有效地提高了学业成绩和学生对解剖学解剖的满意度.
  • 这种人工智能驱动的团队分配策略显示出在其他协作学科中的应用潜力.
  • 通过技术优化团队动态可以显著有利于教育环境.