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

539
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...
539
Hyperbolas01:30

Hyperbolas

570
A hyperbola is a conic section produced when a double-napped cone is intersected by a plane at an angle steeper than the slope of the cone, such that it cuts through both nappes. This intersection yields two separate, mirror-image curves known as branches, which open away from each other along the transverse axis. The nearest points on each branch to the hyperbola’s center are termed vertices, and the distance from the center to a vertex is denoted by a. Perpendicular to the transverse...
570
Vector Transformation in Rotating Coordinate Systems01:16

Vector Transformation in Rotating Coordinate Systems

2.9K
Consider a vector rotating about an axis with an angular velocity, such that its tip sweeps a circular path.
2.9K
Rotation of Asymmetric Top01:11

Rotation of Asymmetric Top

1.8K
By definition, a spherically symmetric body has the same moment of inertia about any axis passing through its center of mass. This situation changes if there is no spherical symmetry. Since most rigid bodies are not spherically symmetric, these require special treatment.
The relationship between the angular momentum of any rigid body and its angular velocity, both of which are vectors, involves the moment of inertia. The moment of inertia is a scalar quantity only for spherically symmetric...
1.8K
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

992
In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
992
Geometry of Hyperbolas01:30

Geometry of Hyperbolas

634
A hyperbola consists of all points where the absolute difference of distances to two fixed points, called foci, remains constant. The standard equation isEach branch extends infinitely and approaches two asymptotes, which guide the curve’s behavior. The parameters a and b define key features: a measures the distance from the center to each vertex along the transverse axis, while b influences the slopes of the asymptotes. The asymptotes have equationsA rectangle centered at the origin with...
634

You might also read

Related Articles

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

Sort by
Same author

A wearable device dataset for mental health assessment using laser Doppler flowmetry and fluorescence spectroscopy sensors.

Communications medicine·2026
Same author

Enabling multi-target drug discovery through latent evolutionary optimization and synthesis-aware prioritization (EVOSYNTH).

Communications chemistry·2026
Same author

DrugPipe: Generative artificial intelligence-assisted virtual screening pipeline for generalizable and efficient drug repurposing.

Biology methods & protocols·2025
Same author

Machine learning for automated electrical penetration graph analysis of aphid feeding behavior: Accelerating research on insect-plant interactions.

PloS one·2025
Same author

ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.

Structural dynamics (Melville, N.Y.)·2024
Same author

Multimodal pretraining for unsupervised protein representation learning.

Biology methods & protocols·2024

Related Experiment Video

Updated: Apr 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

878

EquiHGNN: Scalable rotationally equivariant hypergraph neural networks.

Tien Dang1, Truong-Son Hy1

  • 1Department of Computer Science, The University of Alabama at Birmingham, Birmingham, Alabama 35294, USA.

The Journal of Chemical Physics
|April 8, 2026
PubMed
Summary

This study introduces EquiHGNN, a novel framework using hypergraph neural networks to model complex molecular interactions. It enhances molecular modeling by incorporating symmetry, improving performance on large datasets.

Related Experiment Videos

Last Updated: Apr 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

878

Area of Science:

  • Computational chemistry
  • Machine learning
  • Molecular modeling

Background:

  • Traditional graph models struggle with multi-way molecular interactions.
  • Hypergraphs offer a natural extension for modeling complex, higher-order relationships.

Purpose of the Study:

  • Introduce EquiHGNN, an equivariant hypergraph neural network framework.
  • Improve molecular modeling by integrating symmetry-aware representations.
  • Enhance the robustness and physical meaningfulness of molecular representations.

Main Methods:

  • Developed an equivariant hypergraph neural network (EquiHGNN) framework.
  • Enforced equivariance under relevant transformation groups to preserve properties.
  • Evaluated performance on small and large molecular datasets.

Main Results:

  • Equivariant architectures with symmetry constraints show performance gains.
  • Higher-order interactions outperform 2D graphs for larger molecular systems.
  • Integrating geometric features into higher-order structures further boosts performance.

Conclusions:

  • EquiHGNN provides a robust framework for molecular representation learning.
  • Symmetry and higher-order interactions are crucial for accurate molecular modeling.
  • Geometric information is vital for enhancing performance in molecular systems.