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

Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

507
Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
507
Random Error01:04

Random Error

10.1K
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...
10.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

342
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
342
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

130
Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
130
Variability: Analysis01:11

Variability: Analysis

720
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
720
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Base Metal Photocatalysts: A Bright Future in Photoredox Catalysis?

Organometallics·2026
Same author

Supercooled Goldstone Bosons at the QCD Chiral Phase Transition.

Physical review letters·2026
Same author

Operationalizing passive sensors into scalable, reproducible, neurobehavioral digital markers for Alzheimer's disease: Lessons learned over 10 years.

Digital health·2025
Same author

Dilepton Polarization as a Signature of Plasma Anisotropy.

Physical review letters·2024
Same author

Personalized synthetic MR imaging with deep learning enhancements.

Magnetic resonance in medicine·2022
Same author

Early Assessment of Chemotherapy Response in Advanced Non-Small Cell Lung Cancer with Circulating Tumor DNA.

Cancers·2022

Related Experiment Video

Updated: Apr 13, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K

Principal Component Analysis of Event-by-Event Fluctuations.

Rajeev S Bhalerao1, Jean-Yves Ollitrault2, Subrata Pal3

  • 1Department of Theoretical Physics, Tata Institute of Fundamental Research, Homi Bhabha Road, Mumbai 400005, India.

Physical Review Letters
|May 2, 2015
PubMed
Summary

Principal component analysis reveals new details in heavy-ion collisions. The method clarifies event-by-event fluctuations in multiplicity and anisotropic flow using ALICE data.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

14.7K

Related Experiment Videos

Last Updated: Apr 13, 2026

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

34.5K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials
09:40

Measuring Neural and Behavioral Activity During Ongoing Computerized Social Interactions: An Examination of Event-Related Brain Potentials

Published on: November 15, 2014

14.7K

Area of Science:

  • High-energy physics
  • Nuclear physics

Background:

  • Relativistic heavy-ion collisions create complex particle interactions.
  • Understanding event-by-event fluctuations is crucial for nuclear matter studies.

Purpose of the Study:

  • To apply Principal Component Analysis (PCA) to analyze event-by-event fluctuations.
  • To investigate multiplicity fluctuations and anisotropic flow in heavy-ion collisions.
  • To reveal previously unobserved patterns in particle momentum distributions.

Main Methods:

  • Application of Principal Component Analysis (PCA) to two-particle correlations.
  • Analysis of ALICE data and simulated events from relativistic heavy-ion collisions.
  • Study of elliptic and triangular flow fluctuations versus transverse momentum and rapidity.

Main Results:

  • PCA provides a physically transparent method for extracting information from correlations.
  • New subleading modes were identified in rapidity and transverse momentum.
  • Detailed analysis of elliptic and triangular flow fluctuations was performed.

Conclusions:

  • PCA is a powerful tool for uncovering hidden structures in heavy-ion collision data.
  • The identified subleading modes offer new insights into particle production mechanisms.
  • This approach enhances the understanding of fluctuations in relativistic heavy-ion collisions.