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Stress Concentrations01:24

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Stress concentration is when stress intensifies near discontinuities such as holes or abrupt cross-sectional changes in a structural member. This localized stress can often surpass the average stress within the member. The stress distribution in flat bars, either with a circular hole or varying widths connected by fillets, can be determined experimentally using a photoelastic method. The results are based on ratios of geometric parameters like the ratio of the hole's radius to the smaller...
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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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Thin-walled members with non-symmetrical cross-sections are vital to engineering structures, offering material efficiency and structural integrity. However, unsymmetrical loading on these members leads to complex stress distributions, resulting in simultaneous bending and twisting can cause deformation or structural failure. The interaction between bending and twisting requires detailed analysis to ensure structural resilience.
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Integrins act both as extracellular input receivers and as intracellular processing activators. As their name suggests, integrins are entirely integrated into the membrane structure. Their hydrophobic membrane-spanning regions interact with the phospholipid bilayer's hydrophobic region. These membrane receptors provide extracellular attachment sites for effectors like hormones and growth factors. They activate intracellular response cascades when their effectors are bound and active.
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Destabilization of Microtubules01:45

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The destabilization of microtubules can occur during different stages of the microtubule lifecycle, such as nucleation or elongation. It can take place at either end of the microtubule or in the microtubule lattices as a whole. The lifespan of individual microtubules within a cell varies according to the cell type and stage of the cell cycle. During interphase, the lifespan of the microtubule is about 30 minutes, while during cell division, it is about 15 minutes. In axonal microtubules of...
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Related Experiment Video

Updated: May 24, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
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Structure-and-embedding-based centrality on network fragility in hypergraphs.

Lanlan Chang1, Tian Qiu1, Guang Chen1

  • 1School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China.

Chaos (Woodbury, N.Y.)
|March 3, 2025
PubMed
Summary
This summary is machine-generated.

Identifying critical nodes in hypergraphs is key for network safety. New methods combining structural and embedding features significantly improve network fragility analysis, outperforming existing approaches.

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Area of Science:

  • Network Science
  • Data Mining
  • Machine Learning

Background:

  • Identifying critical nodes is essential for network safety and robustness.
  • Existing methods for vital node identification in hypergraphs often neglect combined structural and embedding information.
  • Hypergraph analysis is gaining importance for modeling complex systems.

Purpose of the Study:

  • To propose novel centrality measures for identifying vital nodes in hypergraphs.
  • To investigate the impact of incorporating node embeddings into structural centrality measures.
  • To evaluate the effectiveness of these new methods in assessing network fragility.

Main Methods:

  • Investigated two topological structural centralities considering common nodes and hyperedges.
  • Developed a hypergraph embedding centrality based on representation learning.
  • Proposed four improved centralities integrating node embeddings with structural properties.
  • Assessed network fragility using six real-world datasets.

Main Results:

  • The proposed methods outperformed baseline approaches in five out of six hypergraphs.
  • Integrating embedding features with structural centralities significantly enhanced performance.
  • Single structure-based centralities showed marked improvement when incorporating embeddings.
  • Node embedding similarity analysis provided heuristic understanding of the results.

Conclusions:

  • Combining hypergraph structure and node embeddings offers a superior approach for vital node identification.
  • The proposed centrality measures are effective in analyzing network fragility.
  • Embedding-enhanced structural centralities represent a promising direction for future research in hypergraph analysis.