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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.6K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.6K
Scaling01:26

Scaling

249
In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
249
Ratio Level of Measurement00:54

Ratio Level of Measurement

18.3K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
18.3K
Sieve Analysis and Grading Curves01:19

Sieve Analysis and Grading Curves

384
Sieve analysis is a method used to determine the particle size distribution of aggregate materials. This process involves the following steps:
384
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Finding Critical Values for Chi-Square01:18

Finding Critical Values for Chi-Square

3.0K
Consider a curve representing sample data drawn randomly from a normally distributed population. One must construct confidence intervals to estimate or to test a claim regarding the population standard deviation. For example, a 95% confidence interval covers 95% of the area under the curve, and the remaining 5% is equally distributed on either side of the curve. To achieve such confidence intervals, one must determine the critical values. The critical values are simply the values separating the...
3.0K

You might also read

Related Articles

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

Sort by
Same author

Classical annealing of the Sherrington-Kirkpatrick spin glass using Suzuki-Kubo mean-field Ising dynamics.

Physical review. E·2025
Same author

Inequalities of energy release rates in compression of nanoporous materials predict its imminent breakdown.

Physical review. E·2025
Same author

Avalanche shapes in the fiber bundle model.

Physical review. E·2024
Same author

Prediction of depinning transitions in interface models using Gini and Kolkata indices.

Physical review. E·2024
Same author

Finding critical points and correlation length exponents using finite size scaling of Gini index.

Physical review. E·2024
Same author

Inequality of avalanche sizes in models of fracture.

Physical review. E·2023
Same journal

Erratum: Bacterial Turbulence at Compressible Fluid Interfaces [Phys. Rev. Lett. 136, 138301 (2026)].

Physical review letters·2026
Same journal

Unveiling Light-Quark Yukawa Flavor Structure via Dihadron Fragmentation at Lepton Colliders.

Physical review letters·2026
Same journal

Adaptable Route to Fast Coherent State Transport via Bang-Bang-Bang Protocols.

Physical review letters·2026
Same journal

Topological Transition and Emergence of Elasticity of Dislocation in Skyrmion Lattice: Beyond Kittel's Magnetic-Polar Analogy.

Physical review letters·2026
Same journal

Pound-Drever-Hall Method for Superconducting-Qubit Readout.

Physical review letters·2026
Same journal

Coupling a ^{73}Ge Nuclear Spin to an Electrostatically Defined Quantum Dot in Silicon.

Physical review letters·2026
See all related articles

Related Experiment Video

Updated: Jul 12, 2025

Detection of Small GTPase Prenylation and GTP Binding Using Membrane Fractionation and GTPase-linked Immunosorbent Assay
13:51

Detection of Small GTPase Prenylation and GTP Binding Using Membrane Fractionation and GTPase-linked Immunosorbent Assay

Published on: November 11, 2018

9.9K

Critical Scaling through Gini Index.

Soumyaditya Das1, Soumyajyoti Biswas1

  • 1Department of Physics, SRM University - AP, Andhra Pradesh - 522240, India.

Physical Review Letters
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

Researchers quantify system criticality using economic inequality measures like the Gini index. This approach simplifies critical exponent analysis and offers a new precursory signal for imminent phase transitions.

More Related Videos

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

9.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Related Experiment Videos

Last Updated: Jul 12, 2025

Detection of Small GTPase Prenylation and GTP Binding Using Membrane Fractionation and GTPase-linked Immunosorbent Assay
13:51

Detection of Small GTPase Prenylation and GTP Binding Using Membrane Fractionation and GTPase-linked Immunosorbent Assay

Published on: November 11, 2018

9.9K
Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
07:11

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella

Published on: May 13, 2019

9.7K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.1K

Area of Science:

  • Physics
  • Complex Systems
  • Statistical Mechanics

Background:

  • Systems exhibiting critical behavior display singularities in response functions at critical points.
  • Response functions typically diverge with universal critical exponents as the driving field approaches its critical value.

Purpose of the Study:

  • To quantify the inequality of response functions using economic measures like the Lorenz curve and Gini index.
  • To explore the universality and predictive power of these inequality measures for critical phenomena.

Main Methods:

  • Constructing Lorenz curves and calculating the Gini index for response functions.
  • Analyzing the scaling of response functions in terms of the Gini index.
  • Utilizing Monte Carlo simulations for the 2D Ising model, site percolation, and fiber bundle model.

Main Results:

  • The Gini index reveals a singularity in response function scaling, demonstrating universality comparable to critical exponents.
  • Critical scaling simplifies to a single-parameter fit, reducing complexity from independent critical point and exponent fitting.
  • The Kolkata index crossing the Gini index provides a precursory signal for criticality.

Conclusions:

  • Economic inequality measures offer a novel and simplified approach to understanding critical phenomena.
  • The identified precursory signal using Gini and Kolkata indices can predict imminent criticality in unknown systems.
  • This methodology has broad applicability across condensed matter, geophysics, biophysics, and atmospheric physics.