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

Stability of structures01:14

Stability of structures

153
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,...
153
Dimensionless Groups in Fluid Mechanics01:15

Dimensionless Groups in Fluid Mechanics

228
Dimensionless groups in fluid mechanics provide simplified ratios that help analyze fluid behavior without relying on specific units. The Reynolds number (Re), which represents the ratio of inertial to viscous forces, distinguishes between laminar and turbulent flows, making it essential in the design of pipelines and aerodynamic surfaces. The Froude number (Fr), the ratio of inertial to gravitational forces, is particularly useful in predicting wave formation and hydraulic jumps in...
228
Typical Model Studies01:30

Typical Model Studies

299
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
299
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

90
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...
90
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.5K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.5K
Network Covalent Solids02:18

Network Covalent Solids

13.3K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
13.3K

You might also read

Related Articles

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

Sort by
Same author

Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Robust Scaling in Human Brain Dynamics Despite Correlated Inputs and Limited Sampling Distortions.

Physical review letters·2026
Same author

Imbalance in gut microbial interactions as a marker of health and disease.

Science (New York, N.Y.)·2026
Same author

Shortest-path percolation on scale-free networks.

Physical review. E·2026
Same author

Integrating Social Determinants of Health in Medical Education: Shifting Future Physicians to Population Health-Centric Thinking and Practice.

Journal of medical education and curricular development·2026
Same author

The Predominant Role of Musical Valence Over Arousal in Pain Modulation: A Psychophysiological Study.

International journal of psychology : Journal international de psychologie·2025
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: May 26, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K

Networks with Many Structural Scales: A Renormalization Group Perspective.

Anna Poggialini1,2, Pablo Villegas2,3, Miguel A Muñoz3,4

  • 1Dipartimento di Fisica Università "Sapienza, ," Piazzale Aldo Moro, 2, I-00185 Rome, Italy.

Physical Review Letters
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

We define scale-invariant networks using constant entropy-loss rate. This helps distinguish scale-free from scale-invariant networks and identifies real-world examples like the human connectome.

More Related Videos

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

8.5K
DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers
08:00

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

6.8K

Related Experiment Videos

Last Updated: May 26, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.1K
Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses
08:55

Methods of Ex Situ and In Situ Investigations of Structural Transformations: The Case of Crystallization of Metallic Glasses

Published on: June 7, 2018

8.5K
DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers
08:00

DNA Nanotubes as a Versatile Tool to Study Semiflexible Polymers

Published on: October 25, 2017

6.8K

Area of Science:

  • Complex systems science
  • Network science
  • Statistical physics

Background:

  • Scale invariance is a fundamental property influencing complex systems.
  • Understanding scale invariance is crucial for network architecture and dynamics.
  • Existing definitions may not fully capture the nuances of scale invariance in networks.

Purpose of the Study:

  • To propose a precise definition of scale-invariant networks.
  • To differentiate between scale-free and scale-invariant network characteristics.
  • To identify and catalog genuinely scale-invariant networks.

Main Methods:

  • Utilizing a renormalization-group coarse-graining framework.
  • Defining scale invariance via a constant entropy-loss rate across scales.
  • Analyzing network structures for scale-invariant properties.

Main Results:

  • A precise definition of scale-invariant networks is established.
  • Clear distinctions are made between scale-free and scale-invariant networks.
  • A comprehensive inventory of scale-invariant networks is provided, including the human connectome.

Conclusions:

  • The proposed framework offers a rigorous way to identify scale-invariant networks.
  • The human connectome displays significant scale-invariant features.
  • Findings advance the study of scale-invariant structures in biological and sociotechnological systems.