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Related Concept Videos

Stereoisomers02:32

Stereoisomers

17.3K
On the basis of mirror symmetry, stereoisomers of an organic molecule can be further classified into diastereomers and enantiomers. Diastereomers are stereoisomers that are not mirror images of each other. Substituted alkenes, such as the cis and trans isomers of 2-butene, are diastereomers, as these molecules exhibit different spatial orientations of their constituent atoms, are not mirror images of each other, and do not interconvert. Here, the interconversion is suppressed due to...
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Stereoisomerism02:52

Stereoisomerism

13.7K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula.
Transition metal complexes often exist as geometric isomers, in which the same atoms are connected through the same types of bonds but with differences in their orientation in space. Coordination complexes with two different ligands in the cis and trans positions from a ligand of interest form isomers. For example, the octahedral [Co(NH3)4Cl2]+ ion has two isomers (Figure 1) In the cis...
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Isomerism02:43

Isomerism

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Isomers are molecules with the same molecular formula but different structural arrangements. Isomers can be further classified into constitutional isomers and stereoisomers. Constitutional isomers differ in the connectivity of their constituent atoms. For example, 2-butanol and diethyl ether are constitutional isomers, as they have the same chemical formula, C4H10O, but differ in the connectivity of the carbon and oxygen atoms. Constitutional isomers have different physical and chemical...
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Stereoisomerism of Cyclic Compounds02:33

Stereoisomerism of Cyclic Compounds

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In this lesson, we delve into the role of ring conformation and its stability, which determines the spatial arrangement and, consequently, the molecular symmetry and stereoisomerism of cyclic compounds. 1,2-Dimethylcyclohexane is used as a case study to evaluate the possible number of stereoisomers. Here, given the multiple (n = 2) chiral centers, there are 2n = 4 possible configurations that lack a plane of symmetry, as the ring skeleton exists in a non-planar chair conformation. In addition,...
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Curvilinear Motion: Polar Coordinates01:27

Curvilinear Motion: Polar Coordinates

753
In polar coordinates, the motion of a particle follows a curvilinear path. The radial coordinate symbolized as 'r,' extends outward from a fixed origin to the particle, while the angular coordinate, 'θ,' measured in radians, represents the counterclockwise angle between a fixed reference line and the radial line connecting the origin to the particle.
The particle's location is described using a unit vector along the radial direction. Deriving the particle's position...
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Unsymmetric Bending01:18

Unsymmetric Bending

723
Unsymmetrical bending occurs when the bending moment applied to a structural member does not align with its principal axis. This misalignment leads to complex stress distributions and deflection patterns that differ from those in symmetrical bending, and are essential for designing structures to withstand different loading conditions. In unsymmetrical bending, the neutral axis—where stress is zero—does not necessarily align with the geometric axes of the cross-section. The...
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Related Experiment Video

Updated: Dec 28, 2025

High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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Learning Compressible 360 ° Video Isomers.

Yu-Chuan Su, Kristen Grauman

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 21, 2020
    PubMed
    Summary

    Researchers developed a convolutional neural network to find optimal sphere rotations for 360° video compression. This method improves compression rates by 8-18% by predicting the most compressible video orientation.

    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Standard video encoders are applied to 360° video, projecting spherical frames arbitrarily.
    • The compressibility of 360° video varies significantly with projection orientation.

    Purpose of the Study:

    • To develop a method for predicting sphere rotations that maximize 360° video compression rates.
    • To leverage machine learning to identify the most compressible orientations without repeated rendering.

    Main Methods:

    • A convolutional neural network was trained to associate visual content with compressibility at different cubemap projection rotations.
    • The model learns to predict the optimal sphere rotation for maximal compression in a single pass.

    Main Results:

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    • "Good" rotations are 8-18% more compressible than "bad" ones.
    • The learning-based approach reliably predicts optimal rotations 78% of the time.
    • Validation was performed on thousands of video clips across multiple codecs.

    Conclusions:

    • Sphere rotation is a significant, previously untapped dimension for 360° video compression.
    • Machine learning offers an efficient and effective solution for optimizing 360° video compression through rotation prediction.