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Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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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.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
116
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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
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Gauss's Law: Problem-Solving01:10

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Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is...
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Calibration Curves: Linear Least Squares01:20

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A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Video Experimental Relacionado

Updated: Sep 9, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
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Aprendizaje de las relaciones secuencia-función con procesos gaussianos escalables e interpretables

Juannan Zhou, Carlos Martí-Gómez, Samantha Petti

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    Resumen
    Este resumen es generado por máquina.

    Desarrollamos modelos de procesos gaussianos interpretables para comprender las relaciones genotipo-fenotipo, explicando la epistasis en grandes conjuntos de datos de secuencias biológicas. Nuestro enfoque ofrece un rendimiento predictivo superior y descubre nuevas interacciones genéticas.

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    Área de la Ciencia:

    • La genética y la bioinformática
    • Biología computacional
    • Biología de sistemas

    Sus antecedentes:

    • Comprender las relaciones genotipo-fenotipo es crucial en genética, pero complicado por la epistasis (efectos mutacionales dependientes del contexto).
    • El fenotipo de alto rendimiento genera grandes conjuntos de datos, pero los modelos estándar luchan con la generalización y la interpretabilidad.
    • Las redes neuronales profundas ofrecen flexibilidad pero carecen de interpretabilidad y cuantificación de incertidumbre.

    Objetivo del estudio:

    • Introducir una nueva familia de modelos de procesos gaussianos interpretables para las relaciones secuencia-función.
    • Para capturar la epistasis utilizando distribuciones previas flexibles que generalizan los modelos de paisaje de fitness.
    • Proporcionar métodos escalables e interpretables para explorar las interacciones genéticas complejas.

    Principales métodos:

    • Desarrolló modelos de procesos gaussianos interpretables con distribuciones previas flexibles para modelar la epistasis.
    • Se han incorporado factores específicos del sitio, del alelo y de la mutación para cuantificar los efectos epistásicos.
    • Aceleración de la GPU utilizada para la escalabilidad a grandes conjuntos de datos (proteína, ARN, SNP de todo el genoma).

    Principales resultados:

    • Logró un rendimiento predictivo superior en grandes conjuntos de datos de secuencias biológicas.
    • Parámetros de modelo generados interpretables que recuperan las características genéticas conocidas.
    • Descubrió nuevas interacciones epistáticas, proporcionando nuevos conocimientos sobre el mapa genotipo-fenotipo.

    Conclusiones:

    • Los modelos de proceso gaussiano desarrollados ofrecen un enfoque escalable e interpretable para estudiar las relaciones secuencia-función.
    • Estos modelos capturan efectivamente la epistasis y proporcionan una visión más profunda del mapa genotipo-fenotipo.
    • Los métodos son aplicables en diversos sistemas biológicos, incluidas las secuencias de ADN, ARN y proteínas.