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

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
Simple...
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.
For example, let X = the...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka 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...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
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.
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...

You might also read

Related Articles

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

Sort by
Same author

Analysis of sparse vector data using tessellation based on root volume-optimal cycles.

Scientific reports·2026
Same author

Machine learning of time series data using persistent homology.

Scientific reports·2025
Same author

Topological data analysis gives two folding paths in HP35(nle-nle), double mutant of villin headpiece subdomain.

Scientific reports·2022
Same author

Protein-Folding Analysis Using Features Obtained by Persistent Homology.

Biophysical journal·2020
Same author

New quantitative method for evaluation of motor functions applicable to spinal muscular atrophy.

Brain & development·2018
Same author

Persistent homology analysis of craze formation.

Physical review. E·2017

Related Experiment Video

Updated: May 18, 2026

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

Bouchaud-Mézard model on a random network.

Takashi Ichinomiya1

  • 1Department of Biomedical Informatics, Gifu University Graduate School of Medicine, Yanagido 1-1, Gifu 501-1194, Japan. tk1miya@gifu-u.ac.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 4, 2012
PubMed
Summary

The Bouchaud-Mézard model on random networks shows wealth condensation occurs at a higher interaction strength (J) than predicted by mean-field theory. This study analytically determines wealth distribution, aligning with simulations.

Related Experiment Videos

Last Updated: May 18, 2026

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

Area of Science:

  • Economic modeling
  • Statistical physics
  • Network science

Background:

  • The Bouchaud-Mézard (BM) model was developed to explain Pareto's law in economic systems.
  • Understanding wealth distribution and condensation is crucial in economic and statistical physics studies.

Purpose of the Study:

  • To analyze the Bouchaud-Mézard model on a random network.
  • To analytically derive the stationary probability distribution function of wealth.
  • To investigate wealth condensation phenomena and compare findings with mean-field theory.

Main Methods:

  • Analytical derivation of the stationary probability distribution function.
  • Application of "adiabatic and independent" assumptions.
  • Comparison with numerical simulations.

Main Results:

  • Wealth condensation, marked by wealth variance divergence, occurs at a higher interaction strength (J) than predicted by mean-field theory.
  • The analytical results show good agreement with numerical simulation outcomes.

Conclusions:

  • The study provides an analytical framework for understanding wealth distribution in the BM model on random networks.
  • The findings highlight the importance of network structure in economic models and reveal discrepancies with simpler mean-field approaches.