Jove
Visualize
Contact Us

Related Concept Videos

Plastic Deformations of Members with a Single Plane of Symmetry01:21

Plastic Deformations of Members with a Single Plane of Symmetry

281
When a structural member undergoes plastic deformation due to bending, it is crucial to understand the position of the neutral axis and the stress distribution. This member, characterized by a single plane of symmetry, exhibits a uniform stress distribution, with negative stress above the neutral axis and positive stress below. Notably, the neutral axis does not align with the centroid of the cross-section. This misalignment is typical in cases where the cross-section is not rectangular or...
281
Deformations in a Symmetric Member in Bending01:18

Deformations in a Symmetric Member in Bending

417
When analyzing the deformation of a symmetric prismatic member subjected to bending by equal and opposite couples, it becomes clear that as the member bends, the originally straight lines on its wider faces curve into circular arcs, with a constant radius centered at a point known as Point C. This phenomenon helps to understand the stress and strain distribution within the member more clearly.
When the member is segmented into tiny cubic elements, it is observed that the primary stress...
417
Conformations of Cyclohexane02:11

Conformations of Cyclohexane

14.9K
Cyclohexane does not exist in a planar form due to the high angle and torsional strain it would experience in the planar structure. Instead, it adopts non-planar chair and boat conformations.
The chair form is the most stable and derives its name from its resemblance to the “easy chair.” In the chair conformation, two carbon atoms are arranged out-of-plane — one above and one below, minimizing the torsional strain. In the chair form, the bond angle is very close to the ideal...
14.9K
Deformation in a Circular Shaft01:10

Deformation in a Circular Shaft

767
One of the distinctive characteristics of circular shafts is their ability to maintain their cross-sectional integrity under torsion. In other words, each cross-section continues to exist as a flat, unaltered entity, simply rotating like a solid, rigid slab. To understand the distribution of shearing stress within such a shaft, consider a cylindrical section inside this circular shaft. This section has a length of L and a radius of R, with one end fixed. The radius of the cylindrical section is...
767
Theorems of Pappus and Guldinus: Problem Solving01:12

Theorems of Pappus and Guldinus: Problem Solving

979
Pappus and Guldinus's theorems are powerful mathematical principles that are used for finding the surface area and volume of composite shapes. For example, consider a cylindrical storage tank with a conical top. Finding the surface area or volume can be challenging for such complex shapes. These theorems are particularly useful in calculating the volume and surface area of such systems. Here, the cylindrical storage tank with a conical top can be broken down into two simple shapes: a...
979
Rotation of Asymmetric Top01:11

Rotation of Asymmetric Top

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Development and Validation of a UPLC-MS/MS for Determination of Loureirin D in Rat Plasma and Its Application to Pharmacokinetics.

Biomedical chromatography : BMC·2026
Same author

Quantification of Bullatine A and Bullatine B in Rat Plasma Using UPLC-MS/MS and Application to Pharmacokinetics.

Biomedical chromatography : BMC·2026
Same author

A Validated UPLC-MS/MS Assay for Isosakuranetin Determination in Rat Plasma and Its Application to Pharmacokinetics.

Biomedical chromatography : BMC·2026
Same author

Advancing solar and wind penetration in China through energy complementarity.

Nature·2026
Same author

On Demographic Group Fairness Guarantees in Deep Learning.

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

Discovery of Novel Heterotetracyclic DNA-Dependent Protein Kinase (DNA-PK) Inhibitors with Improved Oral Bioavailability and Potent Cancer Immunotherapy-Potentiating Activity.

Journal of medicinal chemistry·2026
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 Experiment Video

Updated: Dec 21, 2025

Three-Dimensional Shape Modeling and Analysis of Brain Structures
05:33

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

7.5K

Topology Constrained Shape Correspondence.

Xiang Li, Congcong Wen, Lingjing Wang

    IEEE Transactions on Visualization and Computer Graphics
    |May 15, 2020
    PubMed
    Summary

    Researchers developed TC-NET, a novel network for 3D shape correspondence learning. It enhances performance by preserving topological structure in feature spaces, achieving state-of-the-art results on benchmark datasets.

    Area of Science:

    • Computer Vision
    • Geometric Deep Learning
    • Computational Geometry

    Background:

    • Traditional 3D shape analysis relies on handcrafted descriptors, which struggle with deformation and structural variations.
    • Recent advancements utilize deep neural networks for data-driven learning of point descriptors and shape correspondences, focusing on local structural information.

    Purpose of the Study:

    • To introduce TC-NET, a novel shape correspondence learning network that improves performance by enforcing topological consistency between input shapes and their feature embeddings.
    • To address limitations in current deep learning approaches for 3D shape analysis by incorporating global topological information.

    Main Methods:

    • Calculating topology-associated edge weights to represent the intrinsic topological structure of 3D shapes.

    More Related Videos

    Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
    09:12

    Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

    Published on: August 10, 2017

    7.9K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.1K

    Related Experiment Videos

    Last Updated: Dec 21, 2025

    Three-Dimensional Shape Modeling and Analysis of Brain Structures
    05:33

    Three-Dimensional Shape Modeling and Analysis of Brain Structures

    Published on: November 14, 2019

    7.5K
    Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures
    09:12

    Facet-to-facet Linking of Shape-anisotropic Colloidal Cadmium Chalcogenide Nanostructures

    Published on: August 10, 2017

    7.9K
    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.1K
  • Defining a structural regularization term, termed Topo-Loss, to minimize topology-consistent feature reconstruction loss.
  • Integrating this regularization into a deep neural network for end-to-end shape correspondence learning.

    • Main Results:

    • TC-NET achieved state-of-the-art performance on three standard shape correspondence benchmark datasets.
    • Demonstrated the effectiveness of preserving topological structure in high-dimensional feature spaces for robust correspondence learning.
    • Validated the generalization capability of the topology preservation concept for other shape analysis tasks.

    Conclusions:

    • The proposed TC-NET effectively learns shape correspondences by maintaining topological consistency.
    • The Topo-Loss and topology preservation concept offer a powerful regularization strategy for learning-based 3D shape analysis.
    • This approach advances the robustness and accuracy of 3D shape analysis methods dealing with complex structural variations.