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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

590
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...
590
Network Function of a Circuit01:25

Network Function of a Circuit

1.1K
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
1.1K
Equivalent Resistance01:16

Equivalent Resistance

1.2K
In circuit analysis, situations often arise where resistors are neither in series nor parallel configurations. To tackle such scenarios, three-terminal equivalent networks like the wye (Y) (Figure 1 (a)) or tee (T) and delta (Δ) (Figure 1 (b)) or pi (π) networks come into play. These networks offer versatile solutions and are frequently encountered in various applications, including three-phase electrical systems, electrical filters, and matching networks.
1.2K
Protein Networks02:26

Protein Networks

3.7K
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,...
3.7K
Protein Networks02:26

Protein Networks

1.8K
1.8K
Circuit Terminology01:14

Circuit Terminology

3.1K
An electrical network is a system composed of interconnected elements, such as resistors, capacitors, inductors, and voltage or current sources. Unlike a circuit, an electrical network does not necessarily form a closed path. In other words, while all circuits can be considered networks due to their interconnected nature, not every network qualifies as a circuit.
A circuit, on the other hand, is also an interconnected system of electrical elements but must contain one or more closed paths.
3.1K

You might also read

Related Articles

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

Sort by
Same author

Cognitive-affective and behavioral pain mechanisms in individuals with chronic low back pain: a network analysis.

Pain·2026
Same author

Dysfunctional attitudes in cognitive-behavioral therapy and antidepressant pharmacotherapy for adult depression: A systematic review and meta-analysis of individual participant data.

Journal of consulting and clinical psychology·2026
Same author

Mapping the dynamics of idiographic network models to the network theory of psychopathology.

Behavior research methods·2026
Same author

A theory-construction methodology for network theories in psychology.

Psychological methods·2026
Same author

Non-random patterns in the co-occurrence and accumulation of adverse life events in two national panel datasets.

Communications psychology·2026
Same author

Recognize the Value of the Sum Score, Psychometrics' Greatest Accomplishment.

Psychometrika·2026

Related Experiment Video

Updated: Apr 26, 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

1.7K

A new method for constructing networks from binary data.

Claudia D van Borkulo1, Denny Borsboom2, Sacha Epskamp2

  • 11] Interdisciplinary Center Psychopathology and Emotion regulation, University Medical Center Groningen, University of Groningen [2] Department of Psychology, Psychological Methods, University of Amsterdam.

Scientific Reports
|August 2, 2014
PubMed
Summary

This study introduces a novel, computationally efficient method for assessing network structures using binary data, crucial for fields like psychology. The approach successfully identifies key network features, even with limited sample sizes.

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.2K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Related Experiment Videos

Last Updated: Apr 26, 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

1.7K
A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

3.2K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Area of Science:

  • Psychology
  • Educational Sciences
  • Computational Statistics

Background:

  • Network analysis is increasingly applied in fields with unknown network structures.
  • Existing methods for network structure assessment have limitations, particularly for non-Gaussian or binary data.
  • Accurate network structure assessment is vital for reliable network model application.

Purpose of the Study:

  • To present a computationally efficient method for assessing network structures from binary data.
  • To address the limitations of current methods for non-Gaussian data.
  • To provide a tool for network analysis in fields like psychology and educational sciences.

Main Methods:

  • Development of a network assessment method based on Ising models from physics.
  • Integration of logistic regression with a Goodness-of-Fit measure for model selection.
  • Application to binary data, overcoming computational intractability issues.

Main Results:

  • The proposed method is computationally efficient for estimating network structures from binary data.
  • Validation studies demonstrate success in revealing relevant network features at realistic sample sizes.
  • The method was successfully applied to estimate the network of depression and anxiety symptoms in a large sample.

Conclusions:

  • The presented method offers a viable and efficient approach for network structure assessment with binary data.
  • This technique enhances the applicability of network models in psychology and educational sciences.
  • The method provides a robust tool for identifying complex relationships within networks.