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

Linear time-invariant Systems01:23

Linear time-invariant Systems

1.1K
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
1.1K
First Order Systems01:21

First Order Systems

576
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
576
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

426
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
426
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.1K
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
1.1K
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

373
Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...
373
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

927
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
927

You might also read

Related Articles

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

Sort by
Same author

Event-Triggered Multiple Leaders Formation Tracking for Networked Swarm System With Resilience to Noncooperative Nodes.

IEEE transactions on cybernetics·2025
Same author

Unilateral Graves' disease: a case report with concomitant thyroid cancer and systematic review of literature.

Minerva surgery·2025
Same author

A framework for resilience assessment of transportation networks exposed to geohazard threats.

Risk analysis : an official publication of the Society for Risk Analysis·2025
Same author

A modelling framework to analyze climate change effects on radionuclide aquifer contamination.

Journal of contaminant hydrology·2024
Same author

Multi-Fractal Weibull Adaptive Model for the Remaining Useful Life Prediction of Electric Vehicle Lithium Batteries.

Entropy (Basel, Switzerland)·2023
Same author

An Adaptive Sampling Framework for Life Cycle Degradation Monitoring.

Sensors (Basel, Switzerland)·2023

Related Experiment Video

Updated: Apr 26, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K

A computational framework for prime implicants identification in noncoherent dynamic systems.

Francesco Di Maio1, Samuele Baronchelli, Enrico Zio

  • 1Energy Department, Politecnico di Milano, Via Ponzio 34/3, 20133, Milano, Italy.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|July 22, 2014
PubMed
Summary

This study introduces a new computational framework for dynamic reliability analysis, enhancing traditional methods by incorporating system dynamics and stochastic transitions. It effectively identifies prime implicants in complex systems, improving safety assessments.

Keywords:
Differential evolutiondynamic reliabilitymultiple-valued logicprime implicants

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Related Experiment Videos

Last Updated: Apr 26, 2026

A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.3K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

1.7K

Area of Science:

  • Reliability Engineering
  • System Dynamics
  • Computational Science

Background:

  • Traditional static reliability methods (event trees, fault trees) lack dynamic system behavior analysis.
  • System state transitions and time-dependent interactions are crucial for accurate reliability assessment.

Purpose of the Study:

  • To present a novel computational framework for dynamic reliability analysis.
  • To account for discrete stochastic transition events in system reliability.
  • To identify prime implicants (PIs) within dynamic system models.

Main Methods:

  • Utilizing multiple-valued logic (MVL) for stochastic transitions at discretized times.
  • Employing a differential evolution (DE) algorithm to identify PIs.
  • Formulating a covering problem for MVL accident scenarios.

Main Results:

  • Demonstrated a novel framework for dynamic reliability analysis.
  • Successfully identified prime implicants in a dynamic noncoherent system.
  • Showcased the framework's applicability to practical, time-dependent failure scenarios.

Conclusions:

  • The proposed framework effectively integrates system dynamics and stochastic transitions for reliability analysis.
  • The method provides a robust approach for identifying critical failure scenarios (prime implicants).
  • This dynamic approach enhances traditional reliability engineering methods for complex systems.