Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

800
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...
800
Ethical Issues01:27

Ethical Issues

2.3K
Nurses are essential in patient care, upholding the ethical principles of their profession and effectively navigating ethical dilemmas. Neglecting ethical issues can lead to inadequate patient care, compromised therapeutic relationships, and moral distress among healthcare workers.
Ethical Concerns in Healthcare:
2.3K
Protein Networks02:26

Protein Networks

4.6K
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,...
4.6K
Network Covalent Solids02:18

Network Covalent Solids

16.3K
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...
16.3K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

6.3K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
6.3K
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

296
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
296

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Automatic detection of abrupt transitions in paleoclimate records.

Chaos (Woodbury, N.Y.)·2021
Same author

Noise-driven topological changes in chaotic dynamics.

Chaos (Woodbury, N.Y.)·2021
Same author

Reduced-order models for coupled dynamical systems: Data-driven methods and the Koopman operator.

Chaos (Woodbury, N.Y.)·2021
Same author

Simulating climate with a synchronization-based supermodel.

Chaos (Woodbury, N.Y.)·2018
Same author

Economic networks: Heterogeneity-induced vulnerability and loss of synchronization.

Chaos (Woodbury, N.Y.)·2018
Same author

"FORCE" learning in recurrent neural networks as data assimilation.

Chaos (Woodbury, N.Y.)·2018

Related Experiment Video

Updated: Feb 16, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K

Introduction to focus issue: Synchronization in large networks and continuous media-data, models, and supermodels.

Gregory S Duane1, Carsten Grabow2, Frank Selten3

  • 1Geophysical Institute, University of Bergen, Postbox 7803, 5020 Bergen, Norway.

Chaos (Woodbury, N.Y.)
|January 1, 2018
PubMed
Summary
This summary is machine-generated.

This study explores chaotic system synchronization for data assimilation and supermodeling. Novel time series analysis improves understanding of partially observed systems in fields like climate and economics.

More Related Videos

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Basic Caenorhabditis elegans Methods: Synchronization and Observation
11:34

Basic Caenorhabditis elegans Methods: Synchronization and Observation

Published on: June 10, 2012

49.6K

Related Experiment Videos

Last Updated: Feb 16, 2026

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
07:59

Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons

Published on: June 9, 2023

2.0K
Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

3.3K
Basic Caenorhabditis elegans Methods: Synchronization and Observation
11:34

Basic Caenorhabditis elegans Methods: Synchronization and Observation

Published on: June 10, 2012

49.6K

Area of Science:

  • Complex Systems
  • Applied Mathematics
  • Network Science

Background:

  • Synchronization in loosely coupled chaotic systems is crucial for large-scale modeling.
  • Applications span differential equations, continuous media, and diverse scientific domains.

Purpose of the Study:

  • To explore synchronization applications in data assimilation and supermodeling.
  • To present novel time series analysis for partially observed systems.
  • To bridge synchronization theory with practical applications in climate and macroeconomics.

Main Methods:

  • Focuses on synchronization phenomena in extended systems and their components.
  • Utilizes novel time series analysis techniques.
  • Investigates synchronization for data assimilation and model consensus.

Main Results:

  • Synchronization offers new perspectives on data assimilation from limited observations.
  • Supermodeling achieves partial consensus among disparate models.
  • Improved synchronization description for partially observed, short-time systems.

Conclusions:

  • Synchronization is a key tool for advancing data assimilation and supermodeling.
  • Time series analysis enhances understanding of complex system dynamics.
  • Applications demonstrate broad impact across scientific and economic fields.