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

Time-Series Graph00:54

Time-Series Graph

5.2K
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...
5.2K
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

687
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
687
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
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

1.8K
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
1.8K
Resistors In Series01:10

Resistors In Series

6.6K
A resistor is an ohmic device that limits the flow of charge in a circuit. Most circuits have more than one resistor. If several resistors are connected together and connected to a battery, the current supplied by the battery depends on the equivalent resistance of the circuit. The equivalent resistance of a combination of resistors depends on both their individual values and how they are connected. The simplest combination of resistors is the series combination. 
In a series circuit, the...
6.6K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
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...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Contact temporal network during motility-induced phase separation.

Physical review. E·2025
Same author

Fisher-Shannon Investigation of the Effect of Nonlinearity of Discrete Langevin Model on Behavior of Extremes in Generated Time Series.

Entropy (Basel, Switzerland)·2023
Same author

Tsallis Entropy and Mutability to Characterize Seismic Sequences: The Case of 2007-2014 Northern Chile Earthquakes.

Entropy (Basel, Switzerland)·2023
Same author

Defining the Scale to Build Complex Networks with a 40-Year Norwegian Intraplate Seismicity Dataset.

Entropy (Basel, Switzerland)·2023
Same author

Effect of nonlinearity and persistence on multiscale irreversibility, non-stationarity, and complexity of time series-Case of data generated by the modified Langevin model.

Chaos (Woodbury, N.Y.)·2023
Same author

Assigning Degrees of Stochasticity to Blazar Light Curves in the Radio Band Using Complex Networks.

Entropy (Basel, Switzerland)·2022
Same journal

Dynamical thermalization and turbulence in social stratification models.

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

Endogenous regime switching driven by scalar-irreducible learning dynamics.

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

The coherence analysis and Laplacian spectrum applications of cycle-based iterative networks.

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

Hitting times, recurrence, and local dimension under nonstationary forcing with applications to climate data.

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

Multiscale deep reservoir computing for predicting chaotic dynamical systems.

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

Chaotic decoherence under finite resolution: Lyapunov-controlled interference suppression.

Chaos (Woodbury, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K

Time series analysis in earthquake complex networks.

Denisse Pastén1, Zbigniew Czechowski2, Benjamín Toledo1

  • 1Departamento de Física, Universidad de Chile, Las Palmeras 3425, 653 Santiago, Chile.

Chaos (Woodbury, N.Y.)
|September 6, 2018
PubMed
Summary
This summary is machine-generated.

We developed a new method to analyze seismic activity by converting earthquake data into time series. This approach reveals the spatiotemporal organization of seismic systems and how multifractality changes with large earthquakes.

More Related Videos

The Synthesis, Characterization and Reactivity of a Series of Ruthenium N-triphosPh Complexes
10:51

The Synthesis, Characterization and Reactivity of a Series of Ruthenium N-triphosPh Complexes

Published on: April 10, 2015

12.7K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.1K

Related Experiment Videos

Last Updated: Feb 5, 2026

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K
The Synthesis, Characterization and Reactivity of a Series of Ruthenium N-triphosPh Complexes
10:51

The Synthesis, Characterization and Reactivity of a Series of Ruthenium N-triphosPh Complexes

Published on: April 10, 2015

12.7K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.1K

Area of Science:

  • Geophysics
  • Complex Systems Analysis
  • Network Science

Background:

  • Characterizing seismic complex systems is crucial for understanding earthquake dynamics.
  • Existing methods may not fully capture the spatiotemporal features of seismic processes.

Purpose of the Study:

  • Introduce a novel method for characterizing seismic complex systems.
  • Transform complex networks derived from seismic data into time series for analysis.

Main Methods:

  • Construct undirected complex networks from seismic hypocenter data.
  • Define network nodes by their connectivity.
  • Generate connectivity time series by simulating a walk on the graph based on event occurrence times.

Main Results:

  • Applied the procedure to four seismic datasets from Chile.
  • Demonstrated that the multifractality of the connectivity time series varies with geophysical characteristics of seismic zones.
  • Observed a decrease in multifractality correlated with large earthquake occurrences.

Conclusions:

  • The developed method effectively captures spatiotemporal features of seismic systems.
  • Multifractality analysis of connectivity time series provides insights into seismic zone characteristics and organization.
  • The findings highlight the spatiotemporal organization of seismic systems.