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

514
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...
514
Cascaded Op Amps01:16

Cascaded Op Amps

1.2K
Operational amplifiers (op-amps) are versatile electronic components that can be interconnected in a cascade - one after another in a linear sequence. This cascading is possible due to their infinite input resistance and zero output resistance, allowing them to maintain their input-output relationships even when connected in series.
In a cascaded system, each op-amp is referred to as a stage. The output of one stage drives the input of the subsequent stage. As the input signal passes through...
1.2K
Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

3.3K
After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
With the help of motor proteins such...
3.3K
Multimachine Stability01:25

Multimachine Stability

600
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
600
Cycloaddition Reactions: Overview01:16

Cycloaddition Reactions: Overview

3.7K
Cycloadditions are one of the most valuable and effective synthesis routes to form cyclic compounds. These are concerted pericyclic reactions between two unsaturated compounds resulting in a cyclic product with two new σ bonds formed at the expense of π bonds. The [4 + 2] cycloaddition, known as the Diels–Alder reaction, is the most common. The other example is a [2 + 2] cycloaddition.
3.7K
Network Function of a Circuit01:25

Network Function of a Circuit

974
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.
974

You might also read

Related Articles

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

Sort by
Same author

When Does Population Diversity Matter? A Unified Framework for Binary-Choice Dynamics.

Physical review letters·2025
Same author

Modeling of Jet Lag and Searching for an Optimal Light Treatment.

Journal of biological rhythms·2025
Same author

Strongly clustered random graphs via triadic closure: An exactly solvable model.

Physical review. E·2024
Same author

Nature of hypergraph k-core percolation problems.

Physical review. E·2024
Same author

Theory of percolation on hypergraphs.

Physical review. E·2024
Same author

Retrospective Cohort Study of COVID-19 in Patients of the Brazilian Public Health System with SARS-CoV-2 Omicron Variant Infection.

Vaccines·2022
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
See all related articles

Related Experiment Video

Updated: Mar 8, 2026

Author Spotlight: Efficient Detection of Immune Cell-Infiltration in Cancer Tissues Using Fluorescent Immunohistochemistry
04:21

Author Spotlight: Efficient Detection of Immune Cell-Infiltration in Cancer Tissues Using Fluorescent Immunohistochemistry

Published on: January 26, 2024

1.6K

Cycles and clustering in multiplex networks.

Gareth J Baxter1, Davide Cellai2,3, Sergey N Dorogovtsev1,4

  • 1Department of Physics & I3N, University of Aveiro, Portugal.

Physical Review. E
|January 14, 2017
PubMed
Summary
This summary is machine-generated.

Multiplex networks have complex cycles. This study classifies cycles by layer edges and switches, revealing correlations significantly impact cycle counts and clustering coefficients.

More Related Videos

Design and Use of Multiplexed Chemostat Arrays
19:40

Design and Use of Multiplexed Chemostat Arrays

Published on: February 23, 2013

24.0K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.4K

Related Experiment Videos

Last Updated: Mar 8, 2026

Author Spotlight: Efficient Detection of Immune Cell-Infiltration in Cancer Tissues Using Fluorescent Immunohistochemistry
04:21

Author Spotlight: Efficient Detection of Immune Cell-Infiltration in Cancer Tissues Using Fluorescent Immunohistochemistry

Published on: January 26, 2024

1.6K
Design and Use of Multiplexed Chemostat Arrays
19:40

Design and Use of Multiplexed Chemostat Arrays

Published on: February 23, 2013

24.0K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

11.4K

Area of Science:

  • Network Science
  • Complex Systems
  • Statistical Physics

Background:

  • Traditional network analysis often overlooks the layered structure of multiplex networks.
  • Cycles in multiplex networks are not solely defined by length due to inter-layer edge variations.
  • Understanding cycle structure is crucial for characterizing network topology and dynamics.

Purpose of the Study:

  • To develop a novel classification system for cycles in multiplex networks.
  • To quantify the expected number of different cycle types in large, sparse multiplex networks.
  • To investigate the impact of inter-layer degree correlations on cycle characteristics and clustering coefficients.

Main Methods:

  • Defined a cycle classification based on the number of edges per layer and layer switches.
  • Employed the configuration model to calculate expected cycle counts in multiplex networks.
  • Accounted for the full degree distribution and inter-layer degree correlations.

Main Results:

  • Derived the expected number of cycles for each type in the configuration model.
  • Quantified all possible types of length-3 cycles and their corresponding clustering coefficients.
  • Demonstrated that inter-layer degree correlations significantly influence cycle counts and layer switches, increasing with assortativity and decreasing with disassortativity.

Conclusions:

  • Inter-layer degree correlations are a critical factor in determining multiplex network cycle structure.
  • The proposed classification and methods provide a comprehensive understanding of multiplex network topology.
  • Correlations profoundly affect both cycle counts and clustering coefficients, highlighting their importance in network analysis.