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Related Concept Videos

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.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Random Variables01:09

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
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Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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Consider an angioplasty system featuring a catheter equipped with a turbine, a critical tool for removing plaque deposits from coronary arteries. This intricate medical device operates using a circuit model reminiscent of a dual-node RLC circuit powered by a current-controlled voltage source.
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Circuit Terminology

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Related Experiment Video

Updated: Jun 5, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Exploring the randomness of directed acyclic networks.

Joaquín Goñi1, Bernat Corominas-Murtra, Ricard V Solé

  • 1Department of Neurosciences, Center for Applied Medical Research, University of Navarra, Pamplona, Spain.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
Summary

This study explores directed acyclic graph (DAG) randomization methods for analyzing complex networks. Comparing real-world networks to randomized DAGs helps quantify system randomness and understand network structures.

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Area of Science:

  • Network science
  • Graph theory
  • Computational biology

Background:

  • Feed-forward relationships in time-dependent systems are often modeled using directed acyclic graphs (DAGs).
  • Comparing real-world network architectures to randomized graphs is crucial for quantifying system randomness.
  • Standard randomization methods like the configuration model may be inadequate for finite or highly connected real systems.

Purpose of the Study:

  • To analyze and compare two novel methods for randomizing directed acyclic graphs (DAGs).
  • To investigate DAG randomization techniques that preserve specific topological invariants.
  • To evaluate the impact of randomization methods on network analysis.

Main Methods:

  • Developed and analyzed two DAG randomization algorithms based on preserving directed degree sequence and component distributions.
  • Validated algorithm performance using a highly ordered 'snake graph' and an Erdös-Rényi DAG.
  • Applied randomization methods to three real-world case studies: C. elegans cell lineage, student-supervisor networks, and citation networks.

Main Results:

  • The interpretation of degree-degree relationships in DAGs is significantly influenced by the chosen randomization method and preserved topological invariants.
  • Different randomization approaches yield distinct insights into the structural properties of real-world networks.
  • The study highlights the importance of selecting appropriate null models for network analysis.

Conclusions:

  • The choice of topological invariants to preserve during DAG randomization critically affects the analysis of network properties.
  • The proposed randomization methods offer valuable tools for understanding the structure and randomness of complex systems.
  • Findings emphasize the need for careful consideration of null models in network science research.