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Sequence Networks of Rotating Machines01:24

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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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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Sequences are fundamental mathematical objects consisting of ordered lists of numbers that follow a specific rule or pattern. Sequences are critical in various mathematical concepts, including calculus, series, and number theory. They can model real-world phenomena such as population growth, financial investments, and physical processes like the diminishing height of a bouncing ball.Each number in a sequence is referred to as a term. Typically, the terms are denoted as a1, a2, a3,…, where...
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Per-Unit Sequence Models01:26

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
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In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
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Related Experiment Video

Updated: Mar 22, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Storing Sequences in Binary Tournament-Based Neural Networks.

Xiaoran Jiang, Vincent Gripon, Claude Berrou

    IEEE Transactions on Neural Networks and Learning Systems
    |April 22, 2016
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    Summary
    This summary is machine-generated.

    This study introduces an enhanced clique-based neural network architecture for efficient sequence storage and retrieval. The novel design incorporates oriented connections and redundancies, improving biological plausibility and data handling.

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    Area of Science:

    • Computational Neuroscience
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing neural network architectures face challenges in efficient sequence storage.
    • Clique-based neural networks offer a promising foundation for complex data processing.

    Purpose of the Study:

    • To extend clique-based neural networks for highly efficient sequence storage.
    • To enhance biological plausibility and accuracy in sequence retrieval.

    Main Methods:

    • Introducing oriented connections within the network architecture.
    • Implementing flexible spatial and temporal redundancies.
    • Incorporating a mechanism of anticipation.
    • Proposing a double-layered structure for heteroassociation and autoassociation.

    Main Results:

    • Achieved high efficiency in storing sequences.
    • Demonstrated enhanced biological plausibility of the neural network model.
    • Enabled accurate sequence retrieval through a combined associative structure.

    Conclusions:

    • The extended clique-based neural network architecture significantly improves sequence storage efficiency.
    • The proposed model offers a biologically plausible approach to neural computation.
    • The double-layered structure facilitates precise sequence recall.