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

Short-distance Transport of Resources02:12

Short-distance Transport of Resources

16.6K
Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
16.6K
Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

215
The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
215
Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time

179
When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
One of the key parameters is the mean transit time (MTT), which refers to the total duration required for drug molecules to transit through the body. MTT is determined by calculating the ratio of the area under the moment curve to the area...
179
Column Efficiency: Rate Theory01:12

Column Efficiency: Rate Theory

583
The rate theory of chromatography provides quantitative insight into the shapes and widths of elution bands. These bands are based on the random-walk mechanism governing molecular migration within a column. The Gaussian profile of chromatographic bands arises from the cumulative effect of random molecular motions as they progress through the column.
During elution, a solute molecule experiences numerous transitions between stationary and mobile phases, exhibiting irregular residence times in...
583
Average Velocity01:12

Average Velocity

20.6K
To calculate the other physical quantities in kinematics, we must introduce the time variable. The time variable allows us not only to state the position of the object during its motion, but also how fast it is moving. The speed at which an object is moving is given by the rate at which the position changes with time. For each position xi, we assign a particular time ti. If the details of the motion at each instant are not important, the rate is usually expressed as the average velocity. This...
20.6K
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

290
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
290

You might also read

Related Articles

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

Sort by
Same author

Modeling the Spread of Misfolded Proteins in Alzheimer's Disease using Higher-Order Simplicial Complex Contagion.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Universality in multispecies urban traffic.

Communications physics·2025
Same author

The networks of ingredient combinations as culinary fingerprints of world cuisines.

NPJ science of food·2025
Same author

Drivers of cooperation in social dilemmas on higher-order networks.

Journal of the Royal Society, Interface·2025
Same author

Resilience of science after austerity.

PNAS nexus·2025
Same author

Hyperedge overlap drives synchronizability of systems with higher-order interactions.

Physical review. E·2025

Related Experiment Video

Updated: Oct 11, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.6K

Dynamical efficiency for multimodal time-varying transportation networks.

Leonardo Bellocchi1, Vito Latora2,3, Nikolas Geroliminis4

  • 1Urban Transport Systems Laboratory (LUTS), École Polytechnique Fédérale de Lausanne (EPFL), GC C2 390, Station 18, Lausanne, 1015, Switzerland.

Scientific Reports
|November 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces "dynamical efficiency" to identify traffic congestion hotspots in urban networks. This new measure helps visualize congestion evolution and assess travel choices in complex transportation systems.

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K

Related Experiment Videos

Last Updated: Oct 11, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

3.6K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.6K
Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.2K

Area of Science:

  • Complex Systems Science
  • Network Science
  • Urban Mobility Analysis

Background:

  • Urban transportation networks are dynamic systems prone to congestion due to flow redistribution.
  • Existing network measures often provide a myopic view, focusing on individual segments rather than system-wide conditions.

Purpose of the Study:

  • To develop novel network measures for detecting critical congestion zones and bottlenecks in urban transportation systems.
  • To quantify the impact of congestion on travel time and assess the richness of traveler choices.

Main Methods:

  • Proposed a path-based measure, 'dynamical efficiency,' calculating travel time differences under congested and free-flow conditions.
  • Extended the measure to multilayer networks, introducing a centrality index for inter-modal junctions.
  • Defined the 'dilemma factor' to relate travel time increase to the number of available transportation alternatives.

Main Results:

  • Dynamical efficiency effectively detects and visualizes congestion seeds and their temporal evolution as clusters.
  • The multilayer centrality measure quantifies the importance of inter-modal connections.
  • Macroscopic relationships were found between extra travel time, alternative routes, and congestion levels.

Conclusions:

  • Dynamical efficiency offers a robust method for analyzing urban traffic congestion and network performance.
  • The developed measures provide valuable insights into urban mobility, aiding in the planning and management of transportation systems.
  • The study demonstrates the applicability of these methods using real-world traffic data from a megacity.