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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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.
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Unsymmetric Bending - Angle of Neutral Axis01:15

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Unsymmetrical bending occurs when a structural member is subjected to bending moments in a plane that does not align with the member's principal axes. This scenario typically arises in beams and other structural components when loads are applied at non-ideal angles, introducing complexities in stress analysis.
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Angle of Twist: Problem Solving01:13

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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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Linearization is a mathematical technique used to approximate complex, nonlinear functions with simpler linear models in the vicinity of a chosen reference point. The method is based on the idea that, although a function may be difficult to evaluate exactly, its behavior near a specific input value can often be closely approximated by the tangent line at that point. This approach is particularly useful when small deviations from a known value are involved.Consider the square root function, for...
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A Protocol for Computer-Based Protein Structure and Function Prediction
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Triangular Alignment (TAME): A Tensor-Based Approach for Higher-Order Network Alignment.

Shahin Mohammadi, David F Gleich, Tamara G Kolda

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |August 3, 2016
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    Summary
    This summary is machine-generated.

    We introduce Triangular AlignMEnt (TAME), a novel network alignment method that identifies higher-order structures like triangles. TAME improves accuracy by focusing on conserved substructures over simple edges.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Network Science

    Background:

    • Network alignment is crucial for comparative interactomics.
    • Traditional methods focus on conserved edges and node similarity.
    • Limitations exist in capturing higher-order topological similarities.

    Purpose of the Study:

    • To propose a novel network alignment formulation using higher-order structures.
    • To develop an algorithm maximizing aligned substructures, specifically triangles.
    • To enhance the accuracy and biological relevance of network alignment.

    Main Methods:

    • Formulated network alignment to maximize aligned substructures (triangles).
    • Developed a surrogate objective function solvable via tensor eigenvector problems.
    • Introduced the Triangular AlignMEnt (TAME) algorithm.

    Main Results:

    • TAME demonstrated high node correctness on the NAPAbench dataset.
    • TAME outperformed state-of-the-art methods in conserved triangles for yeast and human interactomes.
    • Conserved triangles showed stronger correlation with node correctness and edge co-expression than conserved edges.

    Conclusions:

    • Maximizing higher-order substructures offers a more effective network alignment strategy.
    • TAME provides a robust method for identifying conserved biological network motifs.
    • The framework is extensible to other arbitrary motifs and higher-order structures.