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

Structure of Benzene: Kekulé Model01:07

Structure of Benzene: Kekulé Model

12.0K
In 1865, August Kekule suggested the structure of benzene according to the structural theory of organic chemistry based on the three assertions—formula of benzene is C6H6, all the hydrogens of benzene are equivalent, and each carbon must have four bonds due to its tetravalency.
He proposed that benzene has a cyclic structure of six carbon atoms attached to one hydrogen atom each, with three alternating pi bonds.
12.0K
Structure of Benzene: Molecular Orbital Model01:18

Structure of Benzene: Molecular Orbital Model

12.7K
According to the molecular orbital (MO) model, benzene has a planar structure with a regular hexagon of six sp2 hybridized carbons. As shown in Figure 1, each carbon is bonded to three other atoms with C–C–C and H–C–C bond angles of 120°. The C–H bond length is 109 pm, and the C–C bond length is 139 pm which is midway between the single bond length of sp3 hybridized carbons (154 pm) and sp2 hybridized carbons (133 pm).
12.7K
Structures of Solids02:22

Structures of Solids

18.0K
Solids in which the atoms, ions, or molecules are arranged in a definite repeating pattern are known as crystalline solids. Metals and ionic compounds typically form ordered, crystalline solids. A crystalline solid has a precise melting temperature because each atom or molecule of the same type is held in place with the same forces or energy. Amorphous solids or non-crystalline solids (or, sometimes, glasses) which lack an ordered internal structure and are randomly arranged. Substances that...
18.0K
Structural Isomerism02:34

Structural Isomerism

21.7K
Isomerism in Complexes
Isomers are different chemical species that have the same chemical formula. Structural isomerism of coordination compounds can be divided into two subcategories, the linkage isomers and coordination-sphere isomers.
Linkage isomers occur when the coordination compound contains a ligand that can bind to the transition metal center through two different atoms. For example, the CN− ligand can bind through the carbon atom or through the nitrogen atom. Similarly, SCN− can...
21.7K
Structure of Lipids03:38

Structure of Lipids

99.1K
Lipids include a diverse group of compounds that are largely nonpolar in nature. This is because they are hydrocarbons that include mostly nonpolar carbon-carbon or carbon-hydrogen bonds. Non-polar molecules are hydrophobic (“water fearing”), or insoluble in water. Lipids perform many different functions in a cell. Cells store energy for long-term use in the form of fats. Lipids also provide insulation from the environment for plants and animals. For example, they help keep aquatic...
99.1K
Viral Structure00:56

Viral Structure

74.7K
Viruses are extraordinarily diverse in shape and size, but they all have several structural features in common. All viruses have a core that contains a DNA- or RNA-based genome. The core is surrounded by a protective coat of proteins called the capsid. The capsid is composed of subunits called capsomeres. The capsid and genome-containing core are together known as the nucleocapsid.
74.7K

You might also read

Related Articles

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

Sort by
Same author

Gaussian level-set percolation on complex networks.

Physical review. E·2024
Same author

Level-set percolation of Gaussian random fields on complex networks.

Physical review. E·2024
Same author

Pulmonary Immune-Related Adverse Events of PD-1 Versus PD-L1 Checkpoint Inhibitors: A Retrospective Review of Pharmacovigilance.

Journal of immunotherapy and precision oncology·2023
Same author

Distribution of the number of cycles in directed and undirected random regular graphs of degree 2.

Physical review. E·2023
Same author

Conformational Heterogeneity and Interchain Percolation Revealed in an Amorphous Conjugated Polymer.

ACS nano·2022
Same author

Cardiovascular Risks with Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors and Monoclonal Antibody Therapy.

Current oncology reports·2022
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Feb 8, 2026

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

2.2K

Structural model for fluctuations in financial markets.

Kartik Anand1, Jonathan Khedair2, Reimer Kühn2

  • 1Deutsche Bundesbank, Wilhelm-Epstein-Strasse 14, 60431 Frankfurt am Main, Germany.

Physical Review. E
|June 17, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new market price model, revealing fat-tailed asset return distributions and explaining volatility clustering through metastable states and transitions. The model offers insights into collective pricing behaviors.

More Related Videos

Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

24.9K
Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
07:42

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

3.6K

Related Experiment Videos

Last Updated: Feb 8, 2026

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

2.2K
Measuring Microbial Mutation Rates with the Fluctuation Assay
07:44

Measuring Microbial Mutation Rates with the Fluctuation Assay

Published on: November 28, 2019

24.9K
Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator
07:42

Rapid Repetition Rate Fluctuation Measurement of Soliton Crystals in a Microresonator

Published on: December 15, 2021

3.6K

Area of Science:

  • Quantitative Finance
  • Statistical Physics
  • Computational Neuroscience

Background:

  • Existing market models like geometric Brownian motion lack realistic complexity.
  • Analog neural network dynamics exhibit glassy properties and metastable states.
  • Macroeconomic conditions influence asset price dynamics.

Purpose of the Study:

  • To develop and analyze a generalized structural model for asset price dynamics.
  • To investigate the impact of interactions on price distributions and volatility clustering.
  • To connect financial market behavior to concepts from statistical physics.

Main Methods:

  • Generating functional analysis with slow driving to simulate macroeconomic effects.
  • Analytical evaluation of asset return distributions across different time scales.
  • Computational simulations to validate model hypotheses.

Main Results:

  • Asset return distributions are analytically shown to be fat-tailed, aligning with empirical data.
  • Interaction-mediated properties predict broader pricing distributions with ferromagnetic bias.
  • Volatility clustering is explained by dynamics within and transitions between metastable states.

Conclusions:

  • The proposed interacting model provides a more realistic framework for asset price dynamics.
  • The model successfully rationalizes fat-tailed distributions and volatility clustering.
  • Financial markets may exhibit emergent properties analogous to glassy systems in physics.