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

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

297
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,...
297
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

154
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
154
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

198
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
198
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

146
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
146
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.6K
The single-compartment model serves as a simplified representation of the human body. This model assumes that the body functions as a single, well-mixed open compartment. When a drug is administered intravenously, it enters the body and quickly distributes uniformly. The drug then undergoes biotransformation and elimination, ultimately leaving the body. The volume of this compartment is referred to as the apparent volume of distribution into which the drug can uniformly distribute. In this...
2.6K
Modeling and Similitude01:12

Modeling and Similitude

376
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
376

You might also read

Related Articles

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

Sort by
Same authorSame journal

Using reactive links to propagate changes across engineering models.

Software and systems modeling·2025
Same author

Model-driven engineering of safety and security software systems: A systematic mapping study and future research directions.

Journal of software (Malden, MA)·2024
Same author

Generating repairs for inconsistent models.

Software and systems modeling·2023
Same author

Variability extraction and modeling for product variants.

Software and systems modeling·2017
Same journal

Formalising privacy regulations with bigraphs.

Software and systems modeling·2026
Same journal

The MDENet education platform: zero-install directed activities for learning MDE.

Software and systems modeling·2026
Same journal

Diagrammatic physical robot models.

Software and systems modeling·2025
Same journal

Extract, model, refine: improved modelling of program verification tools through data enrichment.

Software and systems modeling·2025
Same journal

What makes life for process mining analysts difficult? A reflection of challenges.

Software and systems modeling·2024
See all related articles

Related Experiment Video

Updated: Oct 14, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

Consistent change propagation within models.

Roland Kretschmer1, Djamel Eddine Khelladi2, Roberto Erick Lopez-Herrejon3

  • 1Institute for Software Systems Engineering, Johannes Kepler University, Linz, Austria.

Software and Systems Modeling
|November 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces consistent change propagation to manage model evolution. It ensures developers

Keywords:
Change propagationConsistency detectionInconsistency repairModel-driven engineering

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Related Experiment Videos

Last Updated: Oct 14, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

1.1K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Area of Science:

  • Software Engineering
  • Model-Driven Engineering
  • Software Evolution

Background:

  • Developers modify models with specific intentions like refactoring or defect removal.
  • Unintended consequences of changes can lead to inconsistencies within or across models.
  • Existing research often focuses on repairing inconsistencies as errors, not as cues for further propagation.

Purpose of the Study:

  • To develop an approach for consistent change propagation in model-driven engineering.
  • To leverage inconsistency repair mechanisms to explore the search space of change propagation.
  • To ensure subsequent changes align with and do not contradict earlier developer intentions.

Main Methods:

  • Utilized classical inconsistency repair mechanisms to guide change propagation.
  • Developed a method that suggests repairs for initial inconsistencies and subsequent ones caused by repairs.
  • Ensured that proposed changes maintain consistency with the original developer's intent.

Main Results:

  • Empirically assessed the approach on 18 industrial, academic, and GitHub case studies, demonstrating feasibility and scalability.
  • Comparison with versioned models showed the approach identifies actual repair sequences chosen by developers.
  • An experiment with 22 participants confirmed the approach aligns with developers' workflows for handling changes.

Conclusions:

  • Consistent change propagation is crucial for effective model-driven engineering.
  • The proposed approach effectively guides developers through complex model evolution by managing change propagation.
  • The tool implementation is scalable and aligns with real-world developer practices.