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

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model01:29

Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

17
Pharmacodynamic models are essential tools in understanding the relationship between drug concentrations and their effects on biological systems. By characterizing the dynamics of drug action, these models guide dose selection, optimize therapeutic efficacy, and inform the development of new drugs. Two major classes of pharmacodynamic models include direct effect and indirect response models.Direct Effect ModelsDirect effect models describe the immediate relationship between drug concentration...
17
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

285
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
285
Pharmacodynamic Models: Overview01:27

Pharmacodynamic Models: Overview

19
Pharmacodynamic (PD) responses describe the interaction between a drug and its biological target, culminating in a physiological effect. These responses can be classified into different types: continuous variables, such as blood glucose levels; categorical outcomes, like survival rates; and time-to-event metrics, such as disease progression. Understanding and modeling PD responses are critical for optimizing drug efficacy and safety.PD models describe the relationship between drug concentration...
19
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

408
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...
408
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

610
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,...
610
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

18
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
18

You might also read

Related Articles

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

Sort by
Same author

Probabilistic Evaluation of Integrated Safety-Security Protection Plans for Process Facilities.

Risk analysis : an official publication of the Society for Risk Analysis·2026
Same author

A victim risk identification model for nature-induced urban disaster emergency response.

Risk analysis : an official publication of the Society for Risk Analysis·2024
Same author

Operational safety economics: Foundations, current approaches and paths for future research.

Safety science·2022
Same author

Measuring Safety Culture Using an Integrative Approach: The Development of a Comprehensive Conceptual Framework and an Applied Safety Culture Assessment Instrument.

International journal of environmental research and public health·2022
Same author

The "Transparency for Safety" Triangle: Developing a Smart Transparency Framework to Achieve a Safety Learning Community.

International journal of environmental research and public health·2022
Same author

The development and progress of nanomedicine for esophageal cancer diagnosis and treatment.

Seminars in cancer biology·2022

Related Experiment Video

Updated: Feb 16, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.9K

DAMS: A Model to Assess Domino Effects by Using Agent-Based Modeling and Simulation.

Laobing Zhang1, Gabriele Landucci2, Genserik Reniers1,3,4

  • 1Safety and Security Science Group, Faculty of Technology, Policy and Management, TU Delft, The Netherlands.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|December 22, 2017
PubMed
Summary

Agent-based modeling simulates domino effects in chemical industries, offering a bottom-up view of accident propagation. This approach captures complex interactions and temporal dependencies for better risk assessment.

Keywords:
Agent-based modelingcomputational experimentsdomino effectmajor accident hazard

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K

Related Experiment Videos

Last Updated: Feb 16, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior
06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.9K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K

Area of Science:

  • Chemical Engineering
  • Industrial Safety
  • Complex Systems Modeling

Background:

  • Disastrous accidents in chemical and process industries often involve cascading failures, known as domino effects.
  • Traditional methods for studying domino effects primarily focus on probabilistic network levels.

Purpose of the Study:

  • To propose and validate an agent-based modeling and simulation approach for studying domino effect propagation.
  • To provide a bottom-up perspective on domino effects, unlike traditional network-level analyses.

Main Methods:

  • Modeling installations as agents and their interactions (e.g., heat radiation) via agent rules.
  • Utilizing agent-based modeling (ABM) for a detailed, bottom-up simulation of accident propagation.
  • Applying the developed model to various case studies within the chemical and process industries.

Main Results:

  • The agent-based model successfully simulates higher-level domino effects and synergistic effects.
  • The model effectively accounts for temporal dependencies in accident propagation sequences.
  • Demonstrated ability to model complex, large-scale industrial accident scenarios.

Conclusions:

  • Agent-based modeling offers a powerful tool for understanding and mitigating domino effects in industrial settings.
  • The proposed approach enhances the analysis of accident propagation by incorporating detailed interactions and temporal dynamics.
  • This method is applicable to real-world, large-scale industrial safety challenges.