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

Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Structural Classification of Joints01:20

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...
Functional Classification of Joints01:09

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An immobile...

You might also read

Related Articles

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

Sort by
Same author

Bulldogs stenosis degree classification using synthetic images created by generative artificial intelligence.

Scientific reports·2025
Same author

Retraction notice to "A deep learning approach based on graphs to detect plantation lines" [Heliyon Volume <b>10</b>, Issue 11, 15 June 2024, e31730].

Heliyon·2025
Same author

Application of coincidence index in the discovery of co-expressed metabolic pathways.

Physical biology·2024
Same author

A deep learning approach based on graphs to detect plantation lines.

Heliyon·2024
Same author

Improved wood species identification based on multi-view imagery of the three anatomical planes.

Plant methods·2022
Same author

Mauritia flexuosa palm trees airborne mapping with deep convolutional neural network.

Scientific reports·2021
Same journal

Topological dependence of viral mutation spread in complex host-interaction networks.

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

Multifractal signatures of Hamiltonian chaos in Hyperion's rotational dynamics.

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

Exploring mechanisms for reversal of flow in tunicate hearts.

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

State estimation in spatiotemporal chaos via low-rank StatFEM.

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

Universal response functions in driven dissipative tunneling dynamics.

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

A network-based approach to characterize the dynamics of the coupling field of thermoacoustic oscillators in annular geometry.

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

Related Experiment Video

Updated: May 18, 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

Complex network classification using partially self-avoiding deterministic walks.

Wesley Nunes Gonçalves1, Alexandre Souto Martinez, Odemir Martinez Bruno

  • 1Instituto de Física de São Carlos (IFSC), Universidade de São Paulo, São Carlos, SP - Brazil. wnunes@ursa.ifsc.usp.br

Chaos (Woodbury, N.Y.)
|October 2, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel measurement for classifying complex networks using partially self-avoiding walks. This new method enhances classification accuracy compared to existing topological measurements.

Related Experiment Videos

Last Updated: May 18, 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

Area of Science:

  • Complex systems science
  • Network science
  • Data analysis

Background:

  • Complex networks are crucial in diverse scientific fields.
  • Network topology characterizes connectivity and dynamics.
  • Existing classification measurements are often correlated.

Purpose of the Study:

  • To introduce a new measurement for complex network classification.
  • To address the issue of correlated measurements in network analysis.
  • To improve the accuracy of complex network classification.

Main Methods:

  • Development of a novel measurement based on partially self-avoiding walks.
  • Validation on a large dataset of 40,000 complex networks.
  • Comparison with traditional network topological measurements.

Main Results:

  • The proposed measurement demonstrates improved classification performance.
  • The new method effectively distinguishes between different complex network models.
  • Reduced correlation among measurements is a key advantage.

Conclusions:

  • Partially self-avoiding walks offer a powerful new tool for network classification.
  • The proposed measurement enhances the understanding of network topology.
  • This approach advances the field of complex network analysis.