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

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

Relation between Poisson's ratio, Modulus of Elasticity and Modulus of Rigidity

Deformation occurs in axial and transverse directions when an axial load is applied to a slender bar. This deformation impacts the cubic element within the bar, transforming it into either a rectangular parallelepiped or a rhombus, contingent on its orientation. This transformation process induces shearing strain. Axial loading elicits both shearing and normal strains. Applying an axial load instigates equal normal and shearing stresses on elements oriented at a 45° angle to the load axis.

Mechanistic Models: Overview of Compartment Models

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...

Three-Compartment Open Model

Three-Compartment Open Model

The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...

Stability of structures

Stability of structures

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,...

You might also read

Related Articles

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

Sort by

Same author

Widespread Frequent Methane Emissions From the Oil and Gas Industry in the Permian Basin.

Journal of geophysical research. Atmospheres : JGR·2023

Same author

Towards a classification of bifurcations in Vlasov equations.

Physical review. E·2020

Same author

Balance of Emission and Dynamical Controls on Ozone During the Korea-United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation.

Journal of geophysical research. Atmospheres : JGR·2019

Same author

Mercury Isotopic Fractionation during Pedogenesis in a Tropical Forest Soil Catena (French Guiana): Deciphering the Impact of Historical Gold Mining.

Environmental science & technology·2018

Same author

Particle Interactions Mediated by Dynamical Networks: Assessment of Macroscopic Descriptions.

Journal of nonlinear science·2018

Same author

Bifurcations and Singularities for Coupled Oscillators with Inertia and Frustration.

Physical review letters·2016

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

Hierarchical models of rigidity percolation.

¹Laboratoire JA Dieudonné, UMR CNRS 6621, Université de Nice-Sophia-Antipolis, Parc Valrose, F-06108 Nice Cedex 2, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|April 7, 2010

Summary

This study models rigidity percolation on hierarchical networks, finding no intermediate rigid-floppy phase even with self-organization. This contrasts with random graphs and zero-temperature simulations.

Area of Science:

Statistical physics
Network science
Condensed matter physics

Background:

Rigidity percolation theory investigates the emergence of rigidity in networks.
Hierarchical networks exhibit complex structures relevant to various physical systems.
Self-organization mechanisms can alter network properties and phase transitions.

Purpose of the Study:

To introduce and solve models of generic rigidity percolation in 2D on hierarchical networks.
To investigate the impact of network self-organization on the rigidity phase diagram.
To compare findings with existing studies on random graphs and zero-temperature systems.

Main Methods:

Exact solution using a renormalization transformation.
Modeling generic rigidity percolation.

More Related Videos

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Related Experiment Videos

Last Updated: Jun 14, 2026

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Longitudinal Measurement of Extracellular Matrix Rigidity in 3D Tumor Models Using Particle-tracking Microrheology

Published on: June 10, 2014

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

Published on: February 25, 2015

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

Analysis of hierarchical network structures.

Main Results:

Developed exact solutions for rigidity percolation on hierarchical networks.
Demonstrated that self-organization does not lead to a distinct intermediate rigid-floppy phase.
Observed differences compared to random graph behavior and zero-temperature numerical studies.

Conclusions:

Hierarchical networks with self-organization do not exhibit a true intermediate phase between rigid and floppy states.
The findings challenge some existing theories and numerical results for similar systems.
Rigidity percolation on these specific networks presents a distinct phase diagram.