Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Stability of structures01:14

Stability of structures

160
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
160
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

677
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
677
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

516
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
516
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

121
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
121
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

411
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
411
Distribution Reliability and Automation01:25

Distribution Reliability and Automation

107
Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
107

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

[A case of successful transcatheter aortic valve implantation for severe noncalcified aortic regurgitation].

Zhonghua xin xue guan bing za zhi·2015
Same author

Infrared spectra of small anionic water clusters from density functional theory and wavefunction theory calculations.

Physical chemistry chemical physics : PCCP·2015
Same author

Steering charge kinetics in photocatalysis: intersection of materials syntheses, characterization techniques and theoretical simulations.

Chemical Society reviews·2015
Same author

Genetic diversity of HIV-1 and transmitted drug resistance among newly diagnosed individuals with HIV infection in Hangzhou, China.

Journal of medical virology·2015
Same author

Metformin represses androgen-dependent and androgen-independent prostate cancers by targeting androgen receptor.

The Prostate·2015
Same author

Dietary riboflavin deficiency decreases immunity and antioxidant capacity, and changes tight junction proteins and related signaling molecules mRNA expression in the gills of young grass carp (Ctenopharyngodon idella).

Fish & shellfish immunology·2015

Related Experiment Video

Updated: Jun 23, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Robustness analysis of interdependent network accounting for failure probability and coupling patterns.

Lixin Yang1, Yuanchen Dang1, Gaihui Guo1

  • 1School of Mathematics and Data Science, Shaanxi University of Science and Technology, Xi'an 710021, China.

Chaos (Woodbury, N.Y.)
|June 17, 2024
PubMed
Summary

This study introduces a cascading failure model for interdependent networks. Increasing the overload parameter enhances network robustness but raises costs, while prioritizing high-degree nodes improves resilience.

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.3K

Related Experiment Videos

Last Updated: Jun 23, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
00:07

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

8.3K

Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Systems Engineering

Background:

  • The resilience of interconnected systems is critical for reliable network design and operation.
  • Understanding cascading failures in interdependent networks is essential for mitigating widespread disruptions.

Purpose of the Study:

  • To develop a cascading failure dynamics model for interdependent networks.
  • To analyze the robustness of these networks against various perturbations and failure probabilities.
  • To investigate the influence of network configuration and interdependence types on robustness.

Main Methods:

  • Proposed a failure probability model based on component load distribution to describe node overload and removal.
  • Introduced node capacity cost and average network capacity cost metrics to quantify cascading failure propagation.
  • Conducted numerical simulations to analyze robustness under different interdependence configurations and parameters.

Main Results:

  • Higher overload parameters increase network robustness but also network costs.
  • Allocating protection resources to high-degree nodes significantly enhances network robustness.
  • Multiple-to-multiple interdependent networks demonstrate superior robustness compared to one-to-one networks under similar coupling.

Conclusions:

  • The proposed model effectively captures cascading failure dynamics in interdependent networks.
  • Network design strategies, such as resource allocation and interdependence patterns, critically impact system robustness.
  • Findings provide insights for designing more resilient interdependent network infrastructures.