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

Applications of GIS: Disaster Management and Emergency Response01:29

Applications of GIS: Disaster Management and Emergency Response

181
Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
181
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

125
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
125
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

232
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
232
Survival Tree01:19

Survival Tree

171
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
171
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.4K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.4K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

166
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
166

You might also read

Related Articles

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

Sort by
Same author

Synergistic integration of Fe-MOF and biomass-derived activated carbon for enhanced ciprofloxacin adsorption: experimental insights and DFT-based mechanistic elucidation.

RSC advances·2026
Same author

Preventive efficacy of oxygen therapy against contrast-associated acute kidney injury in patients undergoing coronary angiography: a systematic review and meta-analysis of randomized controlled trials.

BMC nephrology·2026
Same author

Radiological contamination in soils near the world's largest rare earth minerals producing region and their impacts on human health: A summary analysis.

Journal of environmental radioactivity·2026
Same author

Geological and Technical Foundations of Offshore CO<sub>2</sub> Storage in Depleted Reservoirs.

ACS omega·2026
Same author

Correction: Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

Transcatheter Aortic Valve Implantation: British Cardiovascular Intervention Society Position Statement.

Interventional cardiology (London, England)·2026

Related Experiment Video

Updated: Sep 26, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Application of data mining to minimize fire-induced domino effect risks.

Long Ding1,2, Faisal Khan3,4, Jie Ji1

  • 1State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, Anhui, China.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|April 21, 2022
PubMed
Summary

This study introduces data mining (DM) for minimizing fire-induced domino effect risks in chemical plants. It combines evidential failure mode and effects analysis (E-FMEA) with fault tree analysis (FTA) to identify and reduce loss of containment risks.

Keywords:
data miningdomino effectsinherent safetyloss of containmentrisk minimization

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Sep 26, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Chemical Engineering
  • Risk Management
  • Data Mining

Background:

  • Domino effects pose significant risks in chemical processing facilities.
  • Existing risk management strategies often overlook fire-induced domino effects.
  • Data mining (DM) has not been extensively applied to minimize these specific risks.

Purpose of the Study:

  • To investigate the feasibility of using DM for minimizing fire-induced domino effect risks.
  • To develop a data-driven approach for identifying and mitigating loss of containment (LOC) events.
  • To propose unit-specific, evidence-based risk minimization strategies.

Main Methods:

  • Combined evidential failure mode and effects analysis (E-FMEA) with fault tree analysis (FTA) for risk modeling.
  • Utilized industry-specific data (reliability, inspection, maintenance records) to characterize LOC risk.
  • Developed search and statistics rules to mine inspection records for risk factor assessment.
  • Proposed inherent safety strategies based on risk priority number (RPN) scores and tested effectiveness using a probit model.

Main Results:

  • Characterized LOC risk priority number (RPN) for chemical facilities using integrated E-FMEA and FTA.
  • Identified key LOC risk factors through data mining of inspection records.
  • Proposed and validated inherent safety strategies, such as inventory control, for risk minimization.
  • Demonstrated the capability of DM in developing evidence-based, unit-specific risk reduction strategies.

Conclusions:

  • Data mining offers a feasible approach to minimize fire-induced domino effect risks in chemical facilities.
  • The integrated E-FMEA and FTA methodology, enhanced by DM, effectively models and reduces LOC risks.
  • Evidence-based, unit-specific strategies derived from DM can significantly improve operational safety and reduce domino effect consequences.