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

Vector Algebra: Graphical Method01:10

Vector Algebra: Graphical Method

11.6K
Vectors can be multiplied by scalars, added to other vectors, or subtracted from other vectors. The vector sum of two (or more) vectors is called the resultant vector or, for short, the resultant.
We use the laws of geometry to construct resultant vectors, followed by trigonometry to find vector magnitudes and directions. For a geometric construction of the sum of two vectors in a plane, we follow the parallelogram rule. Suppose two vectors are at arbitrary positions. Translate either one of...
11.6K
Velocity and Position by Graphical Method01:34

Velocity and Position by Graphical Method

7.2K
Velocity and position can be calculated from the known function of acceleration as a function of time. The total area under the acceleration-time graph and the velocity-time graph gives the change in velocity and position, respectively. In the case of an airplane, its acceleration is tracked using the inertial navigation system. The pilot provides the input of the airplane's initial position and velocity before takeoff. The inertial navigation system then uses the acceleration data to...
7.2K
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

154
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
154
Time-Series Graph00:54

Time-Series Graph

4.3K
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...
4.3K
Graphical and Analytic Representation of Sinusoids01:20

Graphical and Analytic Representation of Sinusoids

354
Analyzing two sinusoidal voltages with equal amplitude and period but different phases on an oscilloscope, an instrument used to display and analyze waveforms, involves a three-step process.
The first step is measuring the peak-to-peak value, which is twice the amplitude of the sinusoid. This provides information about the maximum voltage swing of the waveform.
Secondly, the period and angular frequency are determined. The period is the time taken for one complete cycle of the waveform, while...
354
Graphing the Wave Function01:13

Graphing the Wave Function

1.6K
Consider the wave equation for a sinusoidal wave moving in the positive x-direction. The wave equation is a function of both position and time. From the wave equation, two different graphs can be plotted.
1.6K

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

Enhancing Quality of Life in Head and Neck Cancer: A Scoping Review on the Role of Physical Prehabilitation.

Cancer medicine·2026
Same author

Prognostic Role of Inflammatory Blood Cell Ratios in Glioblastoma Patients: Insights from a Single-institution Study.

In vivo (Athens, Greece)·2026
Same author

MRI-Based Morphological Features as Predictors of Clinical Outcomes in Locally Advanced Rectal Cancer Treated with Neoadjuvant Chemoradiotherapy: Insights from a Single-Institution Experience.

Journal of clinical medicine·2026
Same author

Learning beyond experience: Generalizing to unseen state space with reservoir computing.

Chaos (Woodbury, N.Y.)·2025
Same author

Is there an impact of autoimmune rheumatological diseases on cutaneous toxicity in breast cancer adjuvant radiotherapy? A mono-institutional experience.

Breast cancer (Tokyo, Japan)·2025
Same author

Route to chaos in multi-species ecosystems.

Chaos (Woodbury, N.Y.)·2025
Same journal

Plasmonic nanocomposite helices for weather-adaptive LiDAR function.

Nature communications·2026
Same journal

Multidirectional strain-insensitive stretchable RF electronics.

Nature communications·2026
Same journal

In-scanner thoughts contribute to resting-state functional connectivity.

Nature communications·2026
Same journal

Metal-center electron affinity modulates multicolor electrochromism in 2D conjugated metal-organic frameworks.

Nature communications·2026
Same journal

Hyperbranched dielectric polymer networks exhibiting giant energy storage density at 250 °C.

Nature communications·2026
Same journal

3D nanoprinting of metals by spatiotemporally confined hot electrons via multiple-electron excitations in nanocrystals.

Nature communications·2026
查看所有相关文章

相关实验视频

Updated: May 21, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

从动态中重建超图形.

Robin Delabays1, Giulia De Pasquale2, Florian Dörfler3

  • 1School of Engineering, University of Applied Sciences of Western Switzerland HES-SO, Sion, Switzerland.

Nature communications
|March 20, 2025
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种新方法,从时间序列数据中推断出复杂的网络结构,包括非对互动. 这种无模型方法可以重建超图和简化复合体,适用于缺乏数学描述的系统.

更多相关视频

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
08:03

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

Published on: May 31, 2022

4.4K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.5K

相关实验视频

Last Updated: May 21, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight
08:03

Advanced Self-Healing Asphalt Reinforced by Graphene Structures: An Atomistic Insight

Published on: May 31, 2022

4.4K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

4.5K

科学领域:

  • 复杂系统分析 复杂系统分析
  • 网络科学 网络科学
  • 计算神经科学是一种计算神经科学.

背景情况:

  • 从动态推断网络结构对于理解复杂系统至关重要.
  • 现有的方法往往难以处理非对式交互,需要详细的系统知识.
  • 需要先进的数学模型来捕捉复杂的系统相互依赖.

研究的目的:

  • 开发一种新的,无模型的算法,用于推断网络结构,包括高阶交互.
  • 从时间序列数据中重建复杂的拓,如超图和简化复杂.
  • 将该方法应用于现实世界的数据,例如大脑活动,以揭示隐藏的网络属性.

主要方法:

  • 使用稀疏识别非线性动态 (SINDy) 进行网络推理.
  • 开发一种算法,从时间序列数据中重建超图和简化复合体.
  • 在Kuramoto和Lorenz动态的合成数据上对该方法进行基准测试.

主要成果:

  • 从合成数据成功地重建了网络结构,包括非对式交互.
  • 证明了算法的无模型性质,不需要先前对节点动态或合函数的知识.
  • 将该方法应用于静止状态脑电图 (EEG) 数据,以推断有效的大脑连接.

结论:

  • 开发的基于SINDy的方法有效地从时间序列数据中推断出复杂的网络结构和非对互动.
  • 这种方法为分析没有已建立的数学模型的系统提供了一个强大的工具,例如生物网络.
  • 非对式相互作用在塑造宏观大脑动态方面发挥着重要作用,EEG数据分析揭示了这一点.