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Related Concept Videos

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

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Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
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People have observed the rolling motion without slipping ever since the invention of the wheel. For example, one can look at the interaction between a car's tires and the surface of the road. If the driver presses the accelerator to the floor so that the tires spin without the car moving forward, there must be kinetic friction between the wheels and the road's surface. If the driver slowly presses the accelerator, causing the car to move forward, the tires roll without slipping. It is...
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An electric motor applies a torque of 700 N·m to an aluminum shaft, triggering a stable rotation. Two pulleys, B and C, are subjected to torques of 300 N·m and 400 N·m, respectively. The modulus of rigidity is provided as 25 GPa. With the knowledge of the length and diameter of each segment, the twist angle between the two pulleys can be computed. First, a section cut is made between pulleys B and C, and the cut cross-section is analyzed using a free-body diagram. Given that the...
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In bromoethane, the three methyl protons are coupled to the two methylene protons that are three bonds away. In accordance with the n+1 rule, the signal from the methyl protons is split into three peaks with 1:2:1 relative intensities. The methylene protons appear as a quartet, with the relative intensities of 1:3:3:1.
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Coupling interactions are strongest between NMR-active nuclei bonded to each other, where spin information can be transmitted directly through the pair of bonding electrons. While nuclei polarize their electrons to the opposite spins, the bonding electron pair has opposite spins. Configurations with antiparallel nuclear spins are expected to be lower in energy. When coupling makes antiparallel states more favorable, J is considered to have a positive value. The one-bond coupling constant, 1J,...
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Related Experiment Video

Updated: May 3, 2026

Experimental Procedure for Warm Spinning of Cast Aluminum Components
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Bidirectional optimization of the melting spinning process.

Xiao Liang, Yongsheng Ding, Zidong Wang

    IEEE Transactions on Cybernetics
    |January 22, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an immune-enhanced neural network for optimizing fiber melting spinning processes. The advanced model improves prediction accuracy and aids in developing new fiber products with desired quality specifications.

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    Area of Science:

    • Materials Science
    • Chemical Engineering
    • Artificial Intelligence

    Background:

    • The melting spinning process involves complex nonlinear relationships between process parameters and fiber quality.
    • Existing neural network models may have limitations in prediction accuracy and solution scope.

    Purpose of the Study:

    • To propose a bidirectional optimizing approach for the melting spinning process using an immune-enhanced neural network.
    • To reveal nonlinear relationships between process configuration and fiber quality indices.
    • To provide a tool for developing new fiber products with specific quality requirements.

    Main Methods:

    • Utilizing a neural network as the core of a bidirectional model.
    • Integrating an artificial immune component to expand the solution search space.
    • Developing a practical software platform for manufacturing implementation.

    Main Results:

    • The immune-enhanced model effectively eliminates approximation errors common in standard neural network models.
    • The bidirectional approach enables optimization in both process configuration and fiber category development.
    • Simulation results demonstrate superior performance compared to ordinary neural network models.

    Conclusions:

    • The proposed immune-enhanced neural network offers a valuable tool for optimizing melting spinning processes.
    • It enhances prediction accuracy and facilitates the development of novel fiber products.
    • The model benefits engineers and decision-makers in the spinning industry.