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

End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

388
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
388
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
125
Prediction Intervals01:03

Prediction Intervals

2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.3K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K
Survival Tree01:19

Survival Tree

117
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
117

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

Network structure of multidimensional frailty among long-term care residents in China: A cross-sectional network analysis.

Belitung nursing journal·2026
Same author

Dual BAFF/APRIL inhibition with telitacicept in systemic lupus erythematosus and IgA nephropathy: pharmacological rationale, clinical efficacy, and safety on female fertility preservation.

Frontiers in pharmacology·2026
Same author

AURKA/PHB2 signaling drives acquired resistance to KRAS <sup>G12C</sup> inhibitors in KRAS <sup>G12C</sup>-mutant NSCLC.

Cell death discovery·2026
Same author

Study on the effect of aquatic exercise combined with hot spring bathing for patients with chronic low back pain: a randomized controlled trial.

BMC sports science, medicine & rehabilitation·2026
Same author

Development and evaluation of an large language model-integrated chatbot intervention for physical activity habit formation in adults with prehypertension.

Digital health·2026
Same author

DLAT suppresses tumor progression and modulates treatment response in colorectal cancer: Insights from a metabolic prognostic model.

Experimental cell research·2026
Same journal

Linking Local Water Electrostatic Potentials to Measured Hydrogen Evolution Onset in Aqueous Electrolytes.

The journal of physical chemistry letters·2026
Same journal

Microsolvation Redirects Electron-Induced Chemistry in Nucleobases.

The journal of physical chemistry letters·2026
Same journal

Interfacial Microenvironment Effects on the Mechanism of Photocatalytic Methanol Conversion for Hydrogen Evolution.

The journal of physical chemistry letters·2026
Same journal

Noncovalent Interactions in Protein-Ti Binding: Titan Bonds at Work.

The journal of physical chemistry letters·2026
Same journal

Partial Phase Remixing of Segregated Mixed Halide Perovskite Nanocrystals Induced by an Instant Change in an External Electric Field.

The journal of physical chemistry letters·2026
Same journal

Pressure-Driven Dissociation of a Kr Clathrate in the Presence of Colloids.

The journal of physical chemistry letters·2026
查看所有相关文章

相关实验视频

Updated: Jul 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

325

一个基于图形神经网络的通用张量预测框架.

Yang Zhong1,2, Hongyu Yu1,2, Xingao Gong1,2,3

  • 1Key Laboratory of Computational Physical Sciences (Ministry of Education), Institute of Computational Physical Sciences, State Key Laboratory of Surface Physics, and Department of Physics, Fudan University, Shanghai 200433, China.

The journal of physical chemistry letters
|July 7, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一个新的图形神经网络 (GNN) 框架,用于预测定向分子性质. 基于边缘的张量预测GNN准确地处理定向数据,扩展GNN应用.

更多相关视频

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

808
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

相关实验视频

Last Updated: Jul 24, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

325
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

808
A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

9.9K

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.
  • 机器学习是机器学习.

背景情况:

  • 图形神经网络 (GNN) 擅长预测不变的分子性质.
  • 由于旋转等差的限制,传统的GNN无法预测方向性质.
  • 这限制了GNN应用程序的标量性质预测.

研究的目的:

  • 为GNN开发一个能够预测方向性质的通用框架.
  • 为了使GNN能够处理不变标量之外的张量属性.
  • 扩大GNN在材料和分子科学中的应用.

主要方法:

  • 提出了一个基于边缘的张量预测图神经网络框架.
  • 以局部空间组件的线性组合作为张量表示,将其投射到边缘方向上.
  • 确保在局部结构内的旋转等差和对称性满足.

主要成果:

  • 成功预测了从第一到第三顺序的各种张量属性.
  • 证明了拟议框架的准确性和普遍性.
  • 验证了框架处理定向属性的能力.

结论:

  • 开发的框架克服了传统GNN在定向性质方面的局限性.
  • 这一进步使GNN能够预测更广泛的物理性质.
  • 该框架为GNN在计算科学中的应用开辟了新的途径.