Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Absolute and Local Extreme Values

Absolute and Local Extreme Values

The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Vector Algebra: Method of Components

Vector Algebra: Method of Components

It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...

Friedman Two-way Analysis of Variance by Ranks

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

Outliers and Influential Points

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the vertical...

You might also read

Related Articles

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

Sort by

Same author

Corrigendum: Use of autologous cord blood mononuclear cells infusion for the prevention of bronchopulmonary dysplasia in extremely preterm neonates: a study protocol for a placebo-controlled randomized multicenter trial [NCT03053076].

Frontiers in pediatrics·2023

Same author

Adaptive irrigation management by Balinese farmers reduces greenhouse gas emissions and increases rice yields.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences·2023

Same author

Characteristics of individuals who frequently use emergency departments in Hong Kong: a region-based cohort study.

Hong Kong medical journal = Xianggang yi xue za zhi·2023

Same author

Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model.

Scientific reports·2021

Same author

Severe myocardial bridge presenting as paroxysmal atrioventricular block.

Journal of postgraduate medicine·2021

Same author

Concentrations of minerals in pig feed ingredients commonly used in China.

Translational animal science·2020

Search research articles

Related Experiment Video

Updated: May 23, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Network extreme eigenvalue: from mutimodal to scale-free networks.

N N Chung¹, L Y Chew, C H Lai

¹Temasek Laboratories, National University of Singapore, Singapore.

Chaos (Woodbury, N.Y.)

|April 3, 2012

Summary

This summary is machine-generated.

This study improves predictions of disease spread in complex networks by accurately calculating extreme eigenvalues. The findings offer better insights into network dynamics and disease transmission thresholds.

More Related Videos

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Related Experiment Videos

Last Updated: May 23, 2026

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Recording and Analyzing Multimodal Large-Scale Neuronal Ensemble Dynamics on CMOS-Integrated High-Density Microelectrode Array

Published on: March 8, 2024

Area of Science:

Network Science
Complex Systems Dynamics
Mathematical Physics

Background:

Extreme eigenvalues of adjacency matrices are key indicators of complex network dynamics.
Previous analytical solutions for ensemble averages of extreme eigenvalues were limited to second-order corrections.
Understanding these eigenvalues is crucial for predicting collective behaviors in networks.

Purpose of the Study:

To analytically determine the ensemble average of extreme eigenvalues in complex networks.
To characterize the deviation of extreme eigenvalues across an ensemble.
To improve the prediction accuracy of epidemic thresholds.

Main Methods:

Utilizing the discrete form of random scale-free networks.
Developing an analytical approximation for the ensemble average of extreme eigenvalues.
Analyzing network robustness and disease vulnerability.

Main Results:

The derived analytical approximation significantly improves upon previous results for extreme eigenvalue ensemble averages.
The improved approximation leads to more accurate predictions of the epidemic threshold.
Bimodal networks, while robust to node removal, exhibit increased vulnerability to disease spread.

Conclusions:

The discrete form of random scale-free networks provides a more accurate method for analyzing extreme eigenvalues.
This research enhances the understanding of network topology's impact on dynamics and disease propagation.
Network structure, particularly bimodality, critically influences disease spreading dynamics and network resilience.