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Related Concept Videos

Network Function of a Circuit01:25

Network Function of a Circuit

Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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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Random Variables01:09

Random Variables

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Fast Decoupled and DC Powerflow

The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
Radical Chain-Growth Polymerization: Chain Branching01:17

Radical Chain-Growth Polymerization: Chain Branching

The skeletal structure of polymers synthesized via radical polymerization is always branched. For example, the polymerization of ethylene by radical polymerization results in a low-density grade of polyethylene with a heavily branched skeletal structure. Here, the radical site abstracts hydrogen from the growing chain, and the radical site shifts from the end (a primary carbon center) to anywhere within the growing chain (a secondary carbon center). Consequently, the part of the chain from the...

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Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
05:24

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy

Published on: January 10, 2025

Multifractal network generator.

Gergely Palla1, László Lovász, Tamás Vicsek

  • 1Statistical and Biological Physics Research Group of the Hungarian Academy of Sciences, Eötvös University, Budapest, Hungary.

Proceedings of the National Academy of Sciences of the United States of America
|April 14, 2010
PubMed
Summary
This summary is machine-generated.

We developed a novel network construction method generating diverse network types with specific statistical properties. This versatile tool aids researchers in modeling complex systems and network data across various scientific fields.

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Generating a Fractal Microstructure of Laminin-111 to Signal to Cells
06:56

Generating a Fractal Microstructure of Laminin-111 to Signal to Cells

Published on: September 28, 2020

Related Experiment Videos

Last Updated: Jun 13, 2026

Multifractal Spectrum Analysis for Assessing Pulmonary Nodule Malignancy
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Published on: January 10, 2025

Generating a Fractal Microstructure of Laminin-111 to Signal to Cells
06:56

Generating a Fractal Microstructure of Laminin-111 to Signal to Cells

Published on: September 28, 2020

Area of Science:

  • Complex Systems
  • Network Science
  • Computational Biology

Background:

  • Realistic network models are crucial for hypothesis testing and data analysis.
  • Existing methods may lack the flexibility to generate diverse network topologies with controlled properties.

Purpose of the Study:

  • Introduce a new, simple yet versatile method for constructing networks with realistic features.
  • Enable the generation of networks with prescribed degree and clustering coefficient distributions.

Main Methods:

  • A novel approach based on mapping singular measures on the unit square to infinite sparse networks.
  • Simultaneous extension to the infinite limit of the singular measure and graph size.
  • Utilizing a simulated annealing process to determine optimal parameters for the generating measure.

Main Results:

  • The method generates a wide variety of network types with controllable statistical properties.
  • Generated graphs exhibit increasing topological structure with system size.
  • Analytic expressions for degree, clustering, and assortativity coefficient distributions are derived.

Conclusions:

  • The proposed method offers a powerful and flexible tool for network construction.
  • Applicable to diverse fields including biology, computer science, and complex systems research.
  • Facilitates the creation of versatile network models for empirical data.