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

Flow Sheet01:17

Flow Sheet

2.3K
Flowsheets are valuable tools in nursing documentation. They enable healthcare professionals to efficiently record and monitor various patient assessments and measurements in a consolidated format.
Here's a closer look at the examples of flowsheets commonly used by nurses:
Graphic Sheet Documentation:
2.3K
Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

208
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
208
Introduction to Types of Flows01:23

Introduction to Types of Flows

1.6K
Fluid flows are categorized by dimensionality and behavior, with one-dimensional flow being the simplest form, where properties like velocity and pressure change only along a single axis. Water moving through straight pipes exemplifies this flow type, as variations in other directions are minimal. One-dimensional analysis helps simplify understanding such flows, focusing solely on changes along the pipe's length.
Two-dimensional flow involves changes in both length and height, as seen in...
1.6K
Flow Cytometry01:23

Flow Cytometry

14.8K
The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
14.8K
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

514
Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
514
Signal Flow Graphs01:18

Signal Flow Graphs

422
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
422

You might also read

Related Articles

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

Sort by
Same author

Reduced IκBα promotes hepatocellular carcinoma cell proliferation and migration via regulation of NF-κB/Erbin axis.

Oncology letters·2020
Same author

Cyclodextrin-derived polymer networks for selective molecular adsorption.

Chemical communications (Cambridge, England)·2020
Same author

Polyadenylation of Histone H3.1 mRNA Promotes Cell Transformation by Displacing H3.3 from Gene Regulatory Elements.

iScience·2020
Same author

Effects of Wuqinxi in the Patients with Chronic Low Back Pain: A Randomized Controlled Trial.

Evidence-based complementary and alternative medicine : eCAM·2020
Same author

Isolated metachronous splenic multiple metastases after colon cancer surgery: A case report and literature review.

World journal of clinical cases·2020
Same author

Acupressure therapy and Liu Zi Jue Qigong for pulmonary function and quality of life in patients with severe novel coronavirus pneumonia (COVID-19): a study protocol for a randomized controlled trial.

Trials·2020
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: Nov 12, 2025

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
09:22

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Published on: October 31, 2011

13.3K

Assessing the information content of complex flows.

Lei Fang1, Nicholas T Ouellette2

  • 1Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania 15620, USA.

Physical Review. E
|March 19, 2021
PubMed
Summary
This summary is machine-generated.

Scientists developed dynamical linear neighborhoods (DLNs) to compress information in complex systems. This method reduces data needed for simulations and experiments, improving accuracy and data compression for turbulent flow.

More Related Videos

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.1K
Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.9K

Related Experiment Videos

Last Updated: Nov 12, 2025

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA
09:22

Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus SCUVA

Published on: October 31, 2011

13.3K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.1K
Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

5.9K

Area of Science:

  • Physics
  • Applied Mathematics
  • Data Science

Background:

  • Complex dynamical systems contain vast information, posing challenges for accurate simulation and experimental measurement.
  • Assessing essential physics information is crucial for determining appropriate numerical discretization and experimental resolution.

Purpose of the Study:

  • To define novel mathematical objects, dynamical linear neighborhoods (DLNs), for information compression in complex systems.
  • To develop a method for compressing the information of a full dynamical system into a minimal set of influential DLNs.

Main Methods:

  • Defined spatiotemporally compact objects termed dynamical linear neighborhoods (DLNs).
  • Applied a set-cover problem approach to identify optimally influential DLNs for information compression.
  • Validated the technique using experimental data from a quasi-two-dimensional turbulent flow.

Main Results:

  • Demonstrated that DLNs effectively reduce the information required to capture local dynamics.
  • Showcased successful compression of complex dynamical system information into a smaller set of DLNs.
  • Applied the method to experimental turbulent flow data, confirming its practical utility.

Conclusions:

  • DLN method provides a robust framework for information compression in complex dynamical systems.
  • The findings have significant implications for assessing simulation/experiment fidelity and compressing large dynamical datasets.
  • This approach enhances the efficiency and accuracy of analyzing complex physical phenomena.