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

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

158
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...
158
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

126
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
126
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

305
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,...
305
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

148
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
148
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

205
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
205
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

265
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
265

You might also read

Related Articles

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

Sort by
Same author

Micro-CT and histological assessment of renal arterial embolization with Glubran®2 cyanoacrylate: a medium-term follow-up study in a rabbit model.

CVIR endovascular·2025
Same author

Embolic Effect of Different Cyanoacrylates: Radiological and Histological Comparison in an In Vivo Rabbit Renal-Artery Model.

Cardiovascular and interventional radiology·2025
Same author

Blocked-flow vs. free-flow cyanoacrylate glue embolization: Histological differences in an in vivo rabbit renal artery model.

Diagnostic and interventional imaging·2024
Same author

An electricity smart meter dataset of Spanish households: insights into consumption patterns.

Scientific data·2024
Same author

Iodixanol as a New Contrast Agent for Cyanoacrylate Embolization: A Preliminary In Vivo Swine Study.

Biomedicines·2023
Same author

In Vitro Characteristics of a Cyanoacrylate/Water-Soluble Contrast Emulsion: Preliminary Data from Light Microscopy Approach.

Cardiovascular and interventional radiology·2023

Related Experiment Video

Updated: Oct 18, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.1K

A Data-Driven Space-Time-Parameter Reduced-Order Model with Manifold Learning for Coupled Problems: Application to

Toufik Boubehziz1, Carlos Quesada-Granja1, Claire Dupont1

  • 1Biomechanics and Bioengineering Laboratory (UMR CNRS 7338), Université de Technologie de Compiègne CNRS, Alliance Sorbonne Université, 60203 Compiègne, France.

Entropy (Basel, Switzerland)
|September 28, 2021
PubMed
Summary

A new data-driven method accurately models microcapsule suspensions in microfluidics for drug delivery. This technique efficiently reduces computational complexity for simulating microcapsule dynamics.

Keywords:
Hausdorff distancedata-driven modeldiffuse approximationmanifold learningmicrocapsule suspensionmodel order reductionproper orthogonal decomposition

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

6.0K
Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales
12:32

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Published on: November 25, 2020

6.7K

Related Experiment Videos

Last Updated: Oct 18, 2025

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression
13:07

Optical Coherence Tomography Based Biomechanical Fluid-Structure Interaction Analysis of Coronary Atherosclerosis Progression

Published on: January 15, 2022

4.1K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.0K
Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales
12:32

Combining Fluidic Devices with Microscopy and Flow Cytometry to Study Microbial Transport in Porous Media Across Spatial Scales

Published on: November 25, 2020

6.7K

Area of Science:

  • Computational fluid dynamics
  • Biomedical engineering
  • Materials science

Background:

  • Microcapsules are crucial for drug delivery, requiring accurate modeling in microfluidic systems.
  • Simulating microcapsule dynamics in microchannels is computationally intensive.
  • Understanding fluid-structure interactions is key for optimizing drug vehicle performance.

Purpose of the Study:

  • To develop an innovative data-driven model-order reduction (MOR) technique for microcapsule suspensions.
  • To accurately simulate microcapsule behavior in microfluidic channels across varying parameters.
  • To enhance the efficiency of modeling microcapsule-based drug delivery systems.

Main Methods:

  • A data-driven MOR technique using global Proper Orthogonal Decomposition (POD) reduced bases for space and parameter variables.
  • Offline computation of reduced bases followed by online computation for new parameter instances.
  • Identification of the nonlinear low-order manifold of reduced variables using diffuse approximation for time evolution.

Main Results:

  • The proposed MOR technique accurately and stably models microcapsule dynamics.
  • Validation through numerical comparisons with a full-order fluid-structure interaction model.
  • Successful reduction of a complex space-time-parameter problem.

Conclusions:

  • The developed MOR technique offers an efficient and accurate approach for modeling microcapsule suspensions.
  • This method has potential applications in healthcare, particularly for drug delivery vehicles.
  • The approach is broadly applicable to coupled problems, especially those involving quasistatic structural mechanics models.