Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

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

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
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
Ver todos los artículos relacionados

Video Experimental Relacionado

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

Actividad emergente hipergrafo temporal: Un modelo para generar hipergrafos realistas dependientes del tiempo

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
Resumen
Este resumen es generado por máquina.

Un nuevo modelo, el Hipergrafo Temporal de Actividad Emergente (EATH), genera hipergrafos temporales sintéticos que imitan las propiedades de los datos empíricos. Esto permite una mejor comprensión de los sistemas complejos y los procesos dinámicos, incluso con datos limitados.

Palabras clave:
hipergrafo temporalredes complejasmodelado generativodinámica de sistemasdatos sintéticos

Más Videos Relacionados

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

Videos de Experimentos Relacionados

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

Área de la Ciencia:

  • Ciencia de Sistemas Complejos
  • Ciencia de Redes
  • Ciencia de Datos

Sus antecedentes:

  • Las interacciones grupales variables en el tiempo son fundamentales para los sistemas complejos.
  • Los hipergrafos temporales capturan interacciones de orden superior y dependientes del tiempo.
  • Los conjuntos de datos empíricos a menudo están incompletos, lo que requiere modelos sustitutos.

Objetivo del estudio:

  • Introducir un novedoso modelo de hipergrafo temporal (EATH) para la generación de conjuntos de datos sintéticos.
  • Permitir el estudio de procesos dinámicos en redes de interacción complejas.
  • Facilitar la comprensión de sistemas con datos de interacción limitados o incompletos.

Principales métodos:

  • Se desarrolló el modelo EATH (Emerging Activity Temporal Hypergraph).
  • EATH utiliza la dinámica de actividad de los nodos y los mecanismos de memoria para generar interacciones.
  • Se validó EATH frente a conjuntos de datos empíricos de interacciones cara a cara.

Principales resultados:

  • EATH generó con éxito hipergrafos temporales sustitutos que reflejan las propiedades de los datos empíricos.
  • Las simulaciones de la dinámica de contagio de orden superior mostraron resultados comparables en datos reales y sintéticos.
  • Se demostró la flexibilidad de EATH en la creación de hipergrafos sintonizables e híbridos.

Conclusiones:

  • El modelo EATH proporciona una herramienta poderosa para crear hipergrafos temporales sintéticos realistas.
  • Los datos sintéticos ayudan en el estudio de la dinámica de sistemas complejos donde la recopilación de datos es un desafío.
  • Abre nuevas vías para la comprensión de comportamientos emergentes en interacciones grupales.