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

Protein Networks02:26

Protein Networks

4.4K
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,...
4.4K
Protein Networks02:26

Protein Networks

2.7K
2.7K
Control Systems01:10

Control Systems

1.7K
Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
1.7K
Open and closed-loop control systems01:17

Open and closed-loop control systems

1.4K
Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...
1.4K
Time-Domain Interpretation of PD Control01:07

Time-Domain Interpretation of PD Control

300
Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...
300
Feedback control systems01:26

Feedback control systems

612
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
612

You might also read

Related Articles

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

Sort by
Same author

Multi-Branch Tree-based Fusion Neural Architecture Search with Zero-Cost Screen for Multi-Modal Classification.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Mining Association Patterns From Neighborhood Insight.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

The Effects of Congruence and Incongruence in Parental Co-Parenting on Adolescents' Depression: Using Polynomial Regression with Response Surface Analysis.

Behavioral sciences (Basel, Switzerland)·2026
Same author

FreqConvMamba: Frequency-guided hierarchical hybrid SSM-CNN for medical image segmentation.

Medical image analysis·2026
Same author

In Situ Heterochiral Helix Coupling Triggered Supramolecular Evolution.

Journal of the American Chemical Society·2026
Same author

Converging dopamine pathways onto basolateral amygdala neurons encode exploration decisions.

Neuron·2026
Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026
Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026
Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026
Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026
Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026
Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026
See all related articles

Related Experiment Video

Updated: Dec 17, 2025

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

2.5K

Target control based on edge dynamics in complex networks.

Furong Lu1,2, Kaikai Yang1,2, Yuhua Qian3,4

  • 1Institute of Big Data Science and Industry, Shanxi University, Taiyuan, 030006, ShanXi, China.

Scientific Reports
|June 21, 2020
PubMed
Summary
This summary is machine-generated.

Researchers developed new methods to control target edges in complex networks. The k-travel and TEC algorithms help identify minimal driven edges and driver nodes for efficient network control.

More Related Videos

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

5.8K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.4K

Related Experiment Videos

Last Updated: Dec 17, 2025

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

2.5K
Quantitative Analysis of Cell Edge Dynamics during Cell Spreading
10:54

Quantitative Analysis of Cell Edge Dynamics during Cell Spreading

Published on: May 22, 2021

5.8K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.4K

Area of Science:

  • Complex network dynamics
  • Network control theory
  • Systems engineering

Background:

  • Controllability of complex networks, particularly nodal dynamics, has been a significant research area.
  • Target control theory provides methods for controlling target nodes in nodal linear dynamic systems.
  • Research on controlling target edges in edge-based dynamics (switchboard dynamics) remains limited.

Purpose of the Study:

  • To propose an effective control scheme for target edges in complex networks.
  • To address the gap in controlling edge dynamics within complex systems.
  • To develop algorithms for minimizing driven edges and driver nodes.

Main Methods:

  • Proposed the k-travel algorithm for directed tree-like networks.
  • Developed the TEC greedy algorithm for general complex networks.
  • Conducted analytic calculations to assess network efficiency in control.

Main Results:

  • The k-travel algorithm approximates minimum driven edges and driver nodes for tree-like networks.
  • The TEC algorithm provides approximations for general network structures.
  • Network properties like assortativity, average shortest path, and clustering coefficient influence control efficiency.

Conclusions:

  • The study introduces novel algorithms for target edge control in complex networks.
  • Network topology significantly impacts the efficiency of random and local target edge control.
  • These findings contribute to advancing the understanding and application of network control theory.