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

Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Second Order systems II01:18

Second Order systems II

In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
If  ζ...
Second Order systems I01:20

Second Order systems I

A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
First Order Systems01:21

First Order Systems

First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...

You might also read

Related Articles

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

Sort by
Same author

Unusual aspect of cuticular morphology in human pulmonary dirofilariasis.

Parasitology international·2026
Same author

First human infection with Onchocerca takaokai (Spirurida: Onchocercidae) presenting as creeping eruption in Japan.

Parasite (Paris, France)·2026
Same author

Interobserver Reliability of the Trampoline Test, Hook Test, and Suction Test in Arthroscopic Assessment of Triangular Fibrocartilage Complex Lesions.

The Journal of hand surgery·2026
Same author

Three-Dimensional In Vivo Kinematic Analysis of Kienböck Disease Treated with Arthroscopic Lunate Excision.

The Journal of hand surgery·2025
Same author

Anatomical study of vulnerable sensory and expendable motor nerves for targeted muscle reinnervation in the upper extremity.

Journal of plastic surgery and hand surgery·2025
Same author

Long-term follow-up of a case of bilateral elbow ulcers in a patient with Werner syndrome treated with pedicled radial forearm flaps.

Case reports in plastic surgery & hand surgery·2025

Related Experiment Video

Updated: Jun 3, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

Classical small systems coupled to finite baths.

Hideo Hasegawa1

  • 1Department of Physics, Tokyo Gakugei University, Koganei, Tokyo 184-8501, Japan. hideohasegawa@goo.jp

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 17, 2011
PubMed
Summary

This study explores energy exchange in classical N(S)-body systems coupled to harmonic oscillator baths. System energy distribution depends mainly on N(S) particles, not bath size N(B).

Area of Science:

  • Statistical mechanics
  • Computational physics
  • Non-equilibrium systems

Background:

  • Investigates classical N(S)-body systems interacting with harmonic oscillator baths.
  • Utilizes an (N(S)+N(B)) model, differing from common N(S)=1 approaches.

Purpose of the Study:

  • Analyze time-dependent energy exchange between system and bath.
  • Characterize the stationary energy distribution of the N(S)-body system.
  • Compare superstatistical approach (SSA) and microcanonical approach (MCA) for nonextensive statistics.

Main Methods:

  • Performed simulations solving 2(N(S)+N(B)) first-order differential equations.
  • Calculated energy exchange for N(S)≈1-10 and N(B)≈10-1000.
  • Analyzed stationary energy distribution f(S)(u) using Γ and q-Γ distributions.

More Related Videos

A Microfluidic System with Surface Patterning for Investigating Cavitation Bubble(s)–Cell Interaction and the Resultant Bioeffects at the Single-cell Level
11:14

A Microfluidic System with Surface Patterning for Investigating Cavitation Bubble(s)–Cell Interaction and the Resultant Bioeffects at the Single-cell Level

Published on: January 10, 2017

Induction of Microstreaming by Nonspherical Bubble Oscillations in an Acoustic Levitation System
08:19

Induction of Microstreaming by Nonspherical Bubble Oscillations in an Acoustic Levitation System

Published on: May 9, 2021

Related Experiment Videos

Last Updated: Jun 3, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

A Microfluidic System with Surface Patterning for Investigating Cavitation Bubble(s)–Cell Interaction and the Resultant Bioeffects at the Single-cell Level
11:14

A Microfluidic System with Surface Patterning for Investigating Cavitation Bubble(s)–Cell Interaction and the Resultant Bioeffects at the Single-cell Level

Published on: January 10, 2017

Induction of Microstreaming by Nonspherical Bubble Oscillations in an Acoustic Levitation System
08:19

Induction of Microstreaming by Nonspherical Bubble Oscillations in an Acoustic Levitation System

Published on: May 9, 2021

Main Results:

  • Observed rapid system energy fluctuations with a slower envelope.
  • Found system energy distribution f(S)(u) primarily depends on N(S), weakly on N(B).
  • Examined effects of bath oscillator coupling and ideal-gas systems.

Conclusions:

  • The N(S)-body system's statistical properties are robust to bath size variations.
  • Provides a critical comparison between SSA and MCA for nonextensive statistics.
  • Highlights the importance of N(S) in determining system behavior.