Jove
Visualize
联系我们

相关概念视频

Time-Series Graph00:54

Time-Series Graph

5.0K
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...
5.0K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

466
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...
466
Graphs of Functions01:30

Graphs of Functions

210
Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
210
Exponential Equations for Modeling Growth02:33

Exponential Equations for Modeling Growth

180
Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is...
180
State Space Representation01:27

State Space Representation

496
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
496
Graphs of Equations in Two Variables01:30

Graphs of Equations in Two Variables

155
An equation with two variables, typically written in the form y = f(x) or Ax + By = C, describes a relationship between quantities represented by x and y. Each solution to such an equation is an ordered pair (x, y) that satisfies the equation when substituted. These pairs can be represented graphically to understand the variables' relationship visually.A common technique for constructing the graph of a two-variable equation is to create a value table. Begin by choosing several values for the...
155

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

Forecasting seasonal influenza epidemics with physics-informed neural networks.

Epidemics·2026
Same author

Preserving friendships in school contacts: An algorithm to construct synthetic temporal networks for epidemic modelling.

PLoS computational biology·2024
Same author

An embedding-based distance for temporal graphs.

Nature communications·2024
Same author

Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach.

Mathematical biosciences·2024
Same author

The temporal dynamics of group interactions in higher-order social networks.

Nature communications·2024
Same author

Infection patterns in simple and complex contagion processes on networks.

PLoS computational biology·2024
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
查看所有相关文章
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

相关实验视频

Updated: Jan 8, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K

新兴活动时间超图:用于生成现实的时间变化的超图的模型.

Marco Mancastroppa1, Giulia Cencetti1, Alain Barrat1

  • 1CPT, CNRS, Université de Toulon, Aix Marseille Univ, Turing Center for Living Systems, 13009 Marseille, France.

Physical review. E
|December 23, 2025
PubMed
概括
此摘要是机器生成的。

一个新的模型,即新兴活动时间超图 (EATH),生成合成时间超图,模仿现实世界的群体互动. 这允许更好地理解复杂的系统和动态过程,即使数据有限.

更多相关视频

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.3K

相关实验视频

Last Updated: Jan 8, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.5K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.3K

科学领域:

  • 复杂系统科学 复杂系统科学
  • 网络科学 网络科学
  • 数据科学数据科学数据科学

背景情况:

  • 时间变化的群体相互作用是复杂系统的基础.
  • 时间超图捕捉了更高阶的,时间依赖的相互作用.
  • 经验数据集往往不完整,需要替代模型.

研究的目的:

  • 引入一种新的时间超图模型 (EATH),用于生成合成数据集.
  • 能够研究复杂交互网络上的动态过程.
  • 促进对具有有限或不完整交互数据的系统的理解.

主要方法:

  • 开发了新兴活动时间超图 (EATH) 模型.
  • EATH使用节点活动动态和内存机制来产生交互.
  • 与经验面对面互动数据集相对应的验证EATH.

主要成果:

  • EATH成功生成了替代时空超图,反映了经验数据属性.
  • 高级传染动态的模拟显示了真实和合成数据的可比结果.
  • 证明了EATH在创建可调和混合超图的灵活性.

结论:

  • EATH模型提供了一个强大的工具,用于创建现实的合成时间超图.
  • 合成数据有助于研究数据收集具有挑战性的复杂系统动态.
  • 开辟了理解群体互动中出现的行为新途径.