Jove
Visualize
Contáctanos

Videos de Conceptos Relacionados

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

32.1K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
32.1K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

4.6K
The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

754
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
754

También podría leer

Artículos Relacionados

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

Ordenar por
Same author

Resilience in collective behaviors of "next generation reservoir computer" oscillators via transmitting signal distortion.

Chaos (Woodbury, N.Y.)·2026
Same author

Nonlinear dynamics of reservoir computing: Theory, realization, and application.

Chaos (Woodbury, N.Y.)·2026
Same author

Chaos, computation and the century of complexity.

Chaos (Woodbury, N.Y.)·2026
Same author

Mapping Aboriginal Mental Health Journeys Through Psychiatric Care Systems.

JAMA network open·2026
Same author

Full-Order Reconstruction of Simplicial Complex Network from Binary Time Series.

Physical review letters·2026
Same author

Bridging mathematical modeling and AI for 3D coordinate recognition of moving objects without external reference and attitude measurement.

Communications engineering·2026
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
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

Video Experimental Relacionado

Updated: Jan 22, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K

Detección de sincronización mediante particiones ordinales espaciales en redes

Zahra Shahriari1, Shannon D Algar1, David M Walker1

  • 1The University of Western Australia, Complex Systems Group, Department of Mathematics and Statistics, Perth, Western Australia, Australia.

Physical review. E
|January 21, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un método novedoso para detectar regiones sincronizadas y comportamientos colectivos en sistemas dinámicos acoplados utilizando patrones ordinales y entropía de permutación. La técnica identifica eficazmente las fronteras de sincronización, incluso en redes complejas y parcialmente sincronizadas.

Palabras clave:
patrones ordinalesentropía de permutaciónsincronizaciónredes complejascomportamiento colectivosistemas dinámicos acoplados

Más Videos Relacionados

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.3K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Videos de Experimentos Relacionados

Last Updated: Jan 22, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.5K
Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks
09:04

Uncovering Beat Deafness: Detecting Rhythm Disorders with Synchronized Finger Tapping and Perceptual Timing Tasks

Published on: March 16, 2015

13.3K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Área de la Ciencia:

  • Sistemas Complejos
  • Ciencia de Redes
  • Teoría de Sistemas Dinámicos

Sus antecedentes:

  • La identificación de comportamientos colectivos en sistemas dinámicos acoplados es crucial para comprender la dinámica de redes complejas.
  • Los métodos existentes pueden tener dificultades con estados parcialmente sincronizados o redes con conectividad no uniforme.

Objetivo del estudio:

  • Desarrollar y validar una metodología robusta para detectar regiones síncronas y clasificar comportamientos colectivos en redes de sistemas dinámicos acoplados.
  • Extender el análisis a redes donde el análisis espaciotemporal tradicional no es factible.

Principales métodos:

  • Utilización de patrones ordinales de configuraciones espaciales de osciladores vecinos para detectar la sincronización en cada punto temporal.
  • Empleo de la entropía de permutación y la cardinalidad de secuencias prohibidas para la clasificación de comportamientos colectivos.
  • Aplicación del método a una red anular de mapas logísticos acoplados y posteriormente a una red con conexiones aleatorias.

Principales resultados:

  • El método detecta con éxito regiones síncronas y confirma hallazgos previos sobre la identificación del comportamiento colectivo.
  • Identifica con precisión los límites de las regiones síncronas en redes parcialmente sincronizadas.
  • Demostró eficacia en una red conectada aleatoriamente, lo que demuestra su utilidad cuando las representaciones espaciotemporales no son factibles.

Conclusiones:

  • La metodología propuesta ofrece un enfoque robusto para analizar la sincronización y los comportamientos colectivos en redes complejas.
  • Proporciona una herramienta potente para caracterizar los estados de la red, particularmente en escenarios con sincronización parcial o topologías complejas.