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Related Concept Videos

Line Loss01:10

Line Loss

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The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
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The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

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According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Reducing Line Loss01:18

Reducing Line Loss

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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.
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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
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Stereoisomerism of Cyclic Compounds02:33

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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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    RefineGAN, a novel deep learning model, accelerates magnetic resonance imaging (MRI) reconstruction. This fast and accurate compressed sensing MRI method significantly improves image quality, even at low sampling rates.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Compressed Sensing MRI (CS-MRI) accelerates image acquisition but relies on slow iterative solvers.
    • Deep learning shows promise in image processing but is underutilized in MRI reconstruction.

    Purpose of the Study:

    • To develop a fast and accurate deep learning model for CS-MRI reconstruction.
    • To address limitations of existing iterative CS-MRI methods.

    Main Methods:

    • Proposed RefineGAN, a generative adversarial network (GAN) based on a fully-residual convolutional autoencoder.
    • Utilized deeper generator/discriminator networks with cyclic data consistency loss.
    • Employed a chained network architecture to enhance reconstruction quality.

    Main Results:

    • RefineGAN achieved extremely rapid reconstruction times (tens of milliseconds).
    • Demonstrated superior image quality, outperforming state-of-the-art methods, especially at low sampling rates (down to 10%).
    • Validated performance across multiple open-source MRI databases.

    Conclusions:

    • RefineGAN offers a significant advancement in fast and accurate CS-MRI reconstruction.
    • The data-driven approach enables high-quality imaging even with severe undersampling.
    • Potential for time-critical clinical applications requiring accelerated MRI acquisition.