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

Reducing Line Loss01:18

Reducing Line Loss

173
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
173
Neural Circuits01:25

Neural Circuits

1.3K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.3K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

125
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...
125
Design Example: Maintaining Level of an Embankment01:19

Design Example: Maintaining Level of an Embankment

92
Constructing a roadway embankment over uneven terrain requires precise leveling to ensure stability and proper drainage. Surveyors use a leveling instrument and staff to calculate ground elevations and determine the required fill material at each point along the embankment alignment.The process begins by positioning a leveling instrument near a benchmark with a known elevation. A backsight reading establishes the instrument height, which serves as a reference for subsequent measurements. A...
92
Survival Tree01:19

Survival Tree

112
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
112
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

88
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
88

You might also read

Related Articles

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

Sort by
Same author

[Full-length transcriptome analysis and identification of the HXK gene family of <i>Lilium tsingtauense</i>].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2026
Same author

rSiglec-10(V set) armed oncolytic adenovirus improves the effects of virotherapy through enhancing oncolysis and antitumor immunity.

International immunopharmacology·2026
Same author

Deciphering small sequence differences in T cell receptor-antigen pairing.

Nature communications·2026
Same author

A Low-Profile Self-Stealth Programmable Metasurface with In-Band and Out-of-Band RCS Reduction.

Research (Washington, D.C.)·2026
Same author

Wavelet Spectrum in a Multi-Channel Network May Reduce Biopsy Rates on Diagnosis of Breast Tumors with BI-RADS Category 4a or Higher.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

Self-assembled micelles stabilized by covalent α-lipoic acid-carboxymethyl chitosan conjugates: Enabling stable delivery and glutathione-responsive release of coenzyme Q10.

Food research international (Ottawa, Ont.)·2026
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jul 21, 2025

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

572

Structure-Aware DropEdge Toward Deep Graph Convolutional Networks.

Jiaqi Han, Wenbing Huang, Yu Rong

    IEEE Transactions on Neural Networks and Learning Systems
    |July 26, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Deep graph convolutional networks (GCNs) suffer from oversmoothing. DropEdge++ enhances GCNs by using layer-dependent and feature-dependent edge sampling, improving performance on node classification tasks.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.2K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    Related Experiment Videos

    Last Updated: Jul 21, 2025

    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

    572
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.2K
    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
    04:48

    Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

    Published on: November 30, 2022

    2.8K

    Area of Science:

    • Graph Neural Networks
    • Machine Learning
    • Deep Learning

    Background:

    • Deep graph convolutional networks (GCNs) face performance degradation due to oversmoothing.
    • Oversmoothing isolates network output from input, reducing expressivity and trainability with increased depth.

    Purpose of the Study:

    • To improve the performance of deep GCNs by addressing the oversmoothing issue.
    • To introduce DropEdge++, an enhanced edge-sampling technique building upon DropEdge.

    Main Methods:

    • Introduced DropEdge++, featuring two structure-aware samplers: layer-dependent (LD) and feature-dependent (FD).
    • Investigated LD sampler, finding increasing edge sampling from the bottom layer superior.
    • Theoretically analyzed phenomenon using mean-edge-number (MEN) metric.
    • FD sampler associates edge sampling probability with node feature similarity, correlating output and input feature spaces.

    Main Results:

    • DropEdge++ achieved superior performance compared to DropEdge and no-drop baselines.
    • LD sampler with increasing edge sampling from the bottom layer outperformed decreasing counterparts.
    • FD sampler further enhanced performance by aligning convergence subspace with input features.
    • Demonstrated efficacy across full- and semi-supervised node classification benchmarks.

    Conclusions:

    • DropEdge++ effectively mitigates oversmoothing in deep GCNs.
    • The proposed LD and FD samplers offer significant improvements in performance and trainability.
    • DropEdge++ is compatible with various GCN backbones, showcasing its versatility.