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

Competition02:34

Competition

25.3K
When organisms require the same limited resources within an environment, they may have to compete for them. Competition is a net-negative interaction. Even if two competing individuals or populations do not interact directly, the overall fitness of both competitors is lowered as a result of not having full access to the limited resource.
25.3K
Protein Networks02:26

Protein Networks

4.7K
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,...
4.7K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

517
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...
517
Microbial Interactions: Competition01:26

Microbial Interactions: Competition

6
Microbial competition is an ecological interaction in which microorganisms vie for limited resources within shared environments. These resources may include nutrients, space, or light, depending on the system. The intensity and outcome of competition are influenced by the environmental context, such as nutrient availability, spatial constraints, and the diversity of microbial species present. These competitive interactions significantly influence the structure, function, and resilience of...
6
Network Function of a Circuit01:25

Network Function of a Circuit

988
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.
988

You might also read

Related Articles

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

Sort by
Same author

Coherence properties of collective modes in ensembles of oscillators.

Physical review. E·2026
Same author

Forecasting seasonal influenza epidemics with physics-informed neural networks.

Epidemics·2026
Same author

Emerging activity temporal hypergraph: A model for generating realistic time-varying hypergraphs.

Physical review. E·2025
Same author

Internal reliability and antireliability in dynamical networks.

Physical review. E·2025
Same author

Mechanics of blunting of actin-myosin interaction dynamics by the actinopathy-causing mutation E334Q in cytoskeletal γ-actin.

The Journal of physiology·2025
Same author

The acquisition of additional control over quorum sensing regulation reduces the variability of final cell density in Burkholderia.

Communications biology·2025
Same journal

Integrated multi-assessment and structural performance index framework for stacking-sequence optimisation of natural fibre reinforced laminates.

Scientific reports·2026
Same journal

SuperiorGAT: graph attention networks for sparse LiDAR point cloud reconstruction in autonomous systems.

Scientific reports·2026
Same journal

The effect of stretching the pectoralis major, sternocleidomastoid, and iliopsoas muscles on 800 m swimming performance in master swimmers.

Scientific reports·2026
Same journal

ISNR-PQC: isometry noise resilience post quantum cryptography primitive.

Scientific reports·2026
Same journal

Identification of high-yielding and stable genotypes of barley in the cold climate of Iran using AMMI and GGE biplot models.

Scientific reports·2026
Same journal

Bayesian negative binomial modelling of spatial and temporal patterns of road traffic deaths in Ghana.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Mar 19, 2026

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

2.7K

The second will be first: competition on directed networks.

Giulia Cencetti1,2, Franco Bagnoli2,3, Francesca Di Patti2,3

  • 1Dipartimento di Ingegneria dell'Informazione and CSDC, università degli Studi di Firenze, via S. Marta 3 I-50139 Firenze, Italy.

Scientific Reports
|June 9, 2016
PubMed
Summary
This summary is machine-generated.

Researchers studied how multiple traps compete on complex networks. They found optimal trap placement is possible by analyzing network topology to minimize interference and maximize efficiency.

More Related Videos

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

1.6K

Related Experiment Videos

Last Updated: Mar 19, 2026

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

2.7K
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

1.6K

Area of Science:

  • Statistical physics
  • Network science
  • Complex systems

Background:

  • Understanding particle or agent behavior on networks is crucial in various scientific fields.
  • Directed complex networks exhibit unique properties due to their asymmetric structure.
  • The concept of 'multiple sinks competition' describes how multiple absorbing sites interact on a network.

Purpose of the Study:

  • To investigate the competitive dynamics between multiple absorbing sinks on directed complex networks.
  • To develop a method for optimally placing these sinks to control agent flux.
  • To quantify the competition between sinks using network topology.

Main Methods:

  • Analysis of a diffusing walker model on directed complex networks.
  • Mathematical quantification of sink competition using topological indicators.
  • Development and testing of a supervised placement protocol for absorbing traps.

Main Results:

  • The asymmetry of directed networks allows for strategic placement of sinks to minimize interference.
  • Analytical indicators were developed to quantify the competition between pairs of sinks based on graph topology.
  • A robust optimization protocol for supervised sink placement was demonstrated using synthetic data.

Conclusions:

  • Optimal placement of multiple sinks on asymmetric, complex networks is achievable.
  • The developed method provides a robust protocol for controlling agent flux through strategic trap positioning.
  • This work offers insights into optimizing capture processes in complex network environments.