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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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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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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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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
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Updated: Aug 11, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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An intrinsically interpretable neural network architecture for sequence to function learning.

Ali Tuğrul Balcı1,2, Mark Maher Ebeid1,2, Panayiotis V Benos3

  • 1Joint Carnegie Mellon University-University of Pittsburgh Program in Computational Biology, Institution, Pittsburgh, 15213, United States and.

Biorxiv : the Preprint Server for Biology
|February 7, 2023
PubMed
Summary
This summary is machine-generated.

We developed tiSFM, a novel deep learning model for sequence-based genomics. tiSFM offers improved performance and intrinsic interpretability for predicting functional genomic readouts.

Area of Science:

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  • Genomics
  • Computational Biology
  • Machine Learning
  • Background:

    • Deep learning models predict genomic functions but lack interpretability.
    • Current methods require computationally intensive post hoc analyses for model explanation.

    Conclusions:

    • tiSFM provides a powerful and interpretable alternative to existing deep learning models for genomics.
    • The model's interpretability facilitates biological discovery in functional genomics.