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

Hazard Rate01:11

Hazard Rate

437
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
437
Hazard Ratio01:12

Hazard Ratio

609
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
609
Protein Networks02:26

Protein Networks

4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
Linear Circuits01:17

Linear Circuits

878
A linear circuit is characterized by its output having a direct proportionality to its input, adhering to the linearity property, which encompasses the principles of homogeneity (scaling) and additivity. Homogeneity dictates that when the input, also referred to as the excitation, is multiplied by a constant factor, the output, known as the response, is correspondingly scaled by the same constant factor. For instance, if the current is multiplied by a constant 'k,' the voltage likewise...
878
Linear Equations01:27

Linear Equations

490
Linear equations form the foundation of many algebraic and real-world applications, characterized by their simplicity and utility. A linear equation is an algebraic statement in which each term is either a constant or a product of a constant and a single variable. These equations represent straight lines when plotted on a Cartesian coordinate plane, reflecting a constant rate of change between two quantities.A typical linear equation in one variable has the form: ax + b = c, where a, b, and c...
490
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K

You might also read

Related Articles

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

Sort by
Same author

Real-world comparison of one- versus two-injection start regimens of aripiprazole once-monthly in bipolar disorder: implications for relapse-related outcomes.

Nordic journal of psychiatry·2026
Same author

Death anxiety and death-related depression in opioid use disorder: relationships with suicidal ideation, impulsivity, and psychological resilience.

BMC psychiatry·2026
Same author

Disentangling stress, anxiety, and depression in the relationship between insomnia and nomophobia among medical students.

Journal of health psychology·2026
Same author

Advanced gravitational decision-making method inspired by newton's law of universal gravitation.

Scientific reports·2026
Same author

Adult Separation Anxiety Disorder in Substance Use Disorder: The Role of Trauma and Attachment Styles in Mediating and Moderating Mechanisms.

Substance use & misuse·2025
Same author

Relationship Between Childhood Trauma, Dissociation, Attachment and Alexithymia in Patients with Bipolar Affective Disorder.

Noro psikiyatri arsivi·2025

Related Experiment Video

Updated: Feb 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K

Hazard analysis using a Bayesian network and linear programming.

Burak Efe1, Mustafa Kurt2, Ömer Faruk Efe3

  • 1Department of Industrial Engineering, Necmettin Erbakan University, Turkey.

International Journal of Occupational Safety and Ergonomics : JOSE
|August 29, 2018
PubMed
Summary

This study uses a Bayesian network and linear programming to identify and manage construction hazards, improving system safety by considering hazard interactions. The integrated approach optimizes resource allocation for effective risk mitigation in iron works.

Keywords:
Bayesian networkconstructionhazard analysislinear programmingoccupational health and safety

More Related Videos

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K
Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
10:30

Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

Published on: October 15, 2014

21.0K

Related Experiment Videos

Last Updated: Feb 6, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.7K
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.5K
Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles
10:30

Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

Published on: October 15, 2014

21.0K

Area of Science:

  • Construction Safety Engineering
  • Risk Management
  • Operations Research

Background:

  • Effective hazard management in construction is crucial for system safety.
  • Identifying and removing single hazards may not adequately address complex interactions.
  • Resource constraints (time, budget) necessitate careful prioritization in safety interventions.

Purpose of the Study:

  • To examine the influence of hazards in construction iron works using a Bayesian network approach.
  • To address the limitations of removing only the most significant hazard by considering interdependencies.
  • To develop a robust decision support system integrating hazard analysis and resource optimization.

Main Methods:

  • Bayesian network modeling to analyze hazard interactions and influences.
  • Linear programming to optimize resource allocation under time and budget constraints.
  • Integration of Bayesian networks and linear programming for a comprehensive decision support system.

Main Results:

  • The proposed Bayesian network effectively models the influence and interactions of construction hazards.
  • Linear programming provided a method to manage resource allocation when facing project constraints.
  • The combined approach created a strong decision support system demonstrated through a construction firm application.

Conclusions:

  • Integrating Bayesian networks and linear programming offers a superior method for construction hazard management.
  • This approach enhances safety by considering hazard interdependencies and optimizing resource use.
  • The demonstrated application highlights the practical utility of the proposed decision support system.