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Related Concept Videos

Types Of Transformers01:16

Types Of Transformers

Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
The Ideal Transformer01:26

The Ideal Transformer

In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's tangential component...
Transformers in Distribution System01:27

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...

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Related Experiment Video

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Identifying influential nodes in brain networks via self-supervised graph-transformer.

Yanqing Kang1, Di Zhu1, Haiyang Zhang1

  • 1Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China.

Computers in Biology and Medicine
|December 28, 2024
PubMed
Summary

This study introduces a novel self-supervised deep learning method to identify influential nodes (I-nodes) in brain networks. The approach effectively uncovers critical brain regions involved in complex network functions, advancing our understanding of brain architecture.

Keywords:
Brain I-nodesBrain functionBrain structureGraph neural networkSelf-supervised

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Area of Science:

  • Neuroscience
  • Brain Imaging
  • Graph Theory
  • Deep Learning

Background:

  • Identifying influential nodes (I-nodes) in brain networks is crucial for understanding brain function.
  • Traditional methods rely on graph theory, potentially missing intrinsic network characteristics.
  • Self-supervised deep learning offers a data-driven approach to explore I-nodes without manual feature engineering.

Purpose of the Study:

  • To propose a novel framework for identifying influential nodes (I-nodes) in brain networks.
  • To leverage self-supervised learning and Graph-Transformer for robust I-node detection.
  • To explore the functional and structural significance of identified I-nodes.

Main Methods:

  • Developed a Self-Supervised Graph Reconstruction framework based on Graph-Transformer (SSGR-GT).
  • Utilized self-supervised learning to extract node importance for reconstruction.
  • Employed Graph-Transformer for capturing local and global brain graph features.
  • Integrated multimodal analysis using graph-based fusion of functional and structural brain data.

Main Results:

  • Identified 56 influential nodes (I-nodes) in critical brain areas like the superior frontal and lateral parietal lobes.
  • These I-nodes demonstrate greater involvement in brain networks, longer fiber connections, and central structural positions.
  • Observed strong functional and structural connectivity, high node efficiency, and overlap with rich-club regions.

Conclusions:

  • The proposed SSGR-GT method effectively identifies influential nodes in brain networks.
  • The findings provide new insights into the role and characteristics of I-nodes.
  • This research enhances the understanding of brain network mechanisms and offers avenues for future studies.