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

4.5K
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,...
4.5K
Key Techniques in Microbiology01:19

Key Techniques in Microbiology

2.3K
Aseptic techniques prevent contamination, ensure experimental accuracy, and protect researchers and microbial cultures. These techniques are essential in clinical, industrial, and research settings where sterility is required.Maintaining Sterility in Laboratory PracticesScientists maintain sterility by sterilizing tools with heat or chemicals, disinfecting work surfaces, and handling cultures in controlled environments. Working near an open flame or within a laminar flow hood reduces the risk...
2.3K
Key Elements for Plant Nutrition02:35

Key Elements for Plant Nutrition

24.2K
Like all living organisms, plants require organic and inorganic nutrients to survive, reproduce, grow and maintain homeostasis. To identify nutrients that are essential for plant functioning, researchers have leveraged a technique called hydroponics. In hydroponic culture systems, plants are grown—without soil—in water-based solutions containing nutrients. At least 17 nutrients have been identified as essential elements required by plants. Plants acquire these elements from the...
24.2K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Antibiotic Selection00:57

Antibiotic Selection

59.9K
Overview
59.9K
What is Natural Selection?01:32

What is Natural Selection?

128.9K
Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
128.9K

You might also read

Related Articles

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

Sort by
Same author

CDK9 degrader induces BRCAness and sensitizes castration-resistant prostate cancer to PARP inhibitor.

Theranostics·2026
Same author

Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-View Geo-Localization.

IEEE transactions on neural networks and learning systems·2026
Same author

Diffusion Graph Transformer for Learning Controllability Robustness in Large-Scale Networks.

IEEE transactions on cybernetics·2026
Same author

Functionalized polymeric nanomaterials for BMP-2 delivery: recent advances toward improved bone regeneration.

Nanomedicine (London, England)·2026
Same author

Brain-Controlled Wheeled Mobile Robots: A Shared Control Framework Integrating Event-Triggered Mechanism and Deep Reinforcement Learning.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

A highly energy-efficient multi-core neuromorphic architecture for training deep spiking neural networks.

Nature communications·2026
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles

Related Experiment Video

Updated: Jan 30, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Key Nodes Selection in Controlling Complex Networks via Convex Optimization.

Jie Ding, Changyun Wen, Guoqi Li

    IEEE Transactions on Cybernetics
    |January 11, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study identifies key network nodes for efficient control using novel algorithms. These methods, including inexact alternating direction method of multipliers (IADMMs), pinpoint crucial nodes in complex networks.

    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
    Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
    11:06

    Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

    Published on: April 7, 2023

    2.8K

    Related Experiment Videos

    Last Updated: Jan 30, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.1K
    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
    Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
    11:06

    Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

    Published on: April 7, 2023

    2.8K

    Area of Science:

    • Network Science
    • Optimization Theory
    • Control Systems Engineering

    Background:

    • Identifying key nodes in large networks for minimal control cost is computationally challenging.
    • Existing methods struggle with the combinatorial complexity of evaluating all possible node subsets.

    Purpose of the Study:

    • To develop efficient algorithms for approximating the identification of key nodes in complex networks.
    • To propose methods that minimize control costs by selecting optimal subsets of nodes.

    Main Methods:

    • Relaxation of Boolean constraints to formulate a convex optimization problem.
    • Development and theoretical convergence analysis of inexact alternating direction method of multipliers (IADMMs).
    • Introduction of degree-based IADMM (D-IADMM) and locally optimized D-IADMM (LD-IADMM) for enhanced performance.

    Main Results:

    • The proposed IADMMs effectively solve the relaxed convex problem.
    • D-IADMM successfully pinpoints key nodes based on degree distribution.
    • LD-IADMM demonstrates significantly improved performance through local optimization.

    Conclusions:

    • The developed algorithms provide effective solutions for identifying key network nodes.
    • The methods are validated across various network types, including Erdős-Rényi, scale-free, and real-world networks.
    • These algorithms offer a practical approach to network control optimization.