Jove
Visualize
Contact Us

Related Concept Videos

One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model01:15

One-Compartment Open Model for Extravascular Administration: First-Order Absorption Model

277
The first-order absorption model for extravascular administration describes the rate at which a drug is absorbed and eliminated, following the principles of first-order kinetics. This model is vital as it provides a mathematical representation of drug behavior within the body. It also allows for the prediction and interpretation of drug absorption and elimination based on the rate of change in drug concentration over time. This model can be visualized as a plasma concentration-time profile...
277
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.8K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.8K
Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

135
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
135
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

525
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
525
Compartment Models: Single-Compartment Model01:14

Compartment Models: Single-Compartment Model

2.4K
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...
2.4K
Two-Compartment Open Model: Extravascular Administration01:12

Two-Compartment Open Model: Extravascular Administration

269
The two-compartment model for extravascular administration represents a drug's absorption and distribution process. It features a central compartment, where the drug is first absorbed, and a peripheral compartment, which illustrates the drug's distribution throughout the body. The rate of change in drug concentration in the central compartment is calculated by three exponents: absorption, distribution, and elimination.
The absorption exponent (ka) indicates the speed at which the drug...
269

You might also read

Related Articles

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

Sort by
Same author

Intelligent Method of Identifying the Nonlinear Dynamic Model for Helicopter Turboshaft Engines.

Sensors (Basel, Switzerland)·2024
Same author

Cannabidiol Intervention for Muscular Tension, Pain, and Sleep Bruxism Intensity-A Randomized, Double-Blind Clinical Trial.

Journal of clinical medicine·2024
Same author

Integration of Discrete Simulation, Prediction, and Optimization Methods for a Production Line Digital Twin Design.

Materials (Basel, Switzerland)·2023
Same author

Condition-Based Failure-Free Time Estimation of a Pump.

Sensors (Basel, Switzerland)·2023
Same author

Production Planning Using a Shared Resource Register Organized According to the Assumptions of Blockchain Technology.

Sensors (Basel, Switzerland)·2023
Same author

On Transient Queue-Size Distribution in a Model of WSN Node with Threshold-Type Power-Saving Algorithm.

Sensors (Basel, Switzerland)·2022
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 Experiment Video

Updated: Aug 16, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.0K

Transient Behavior of a Queueing Model with Hyper-Exponentially Distributed Processing Times and Finite Buffer

Wojciech M Kempa1, Iwona Paprocka2

  • 1Department of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, 23 Kaszubska Str., 44-100 Gliwice, Poland.

Sensors (Basel, Switzerland)
|December 23, 2022
PubMed
Summary

This study introduces a queueing model with finite capacity, Poisson arrivals, and hyper-exponential service times. It provides a mathematical solution for analyzing queue size distribution over time.

Keywords:
digital twindisassembly sequencingend-of-line productfinite capacityhyper-exponential distributionqueue-size distributiontransient analysis

More Related Videos

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.5K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Related Experiment Videos

Last Updated: Aug 16, 2025

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0
07:41

Modeling Fast-scan Cyclic Voltammetry Data from Electrically Stimulated Dopamine Neurotransmission Data Using QNsim1.0

Published on: June 5, 2017

10.0K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

10.5K
Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
13:00

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

10.0K

Area of Science:

  • Operations Research
  • Applied Probability
  • Queueing Theory

Background:

  • Queueing models are essential for analyzing waiting lines in various systems.
  • Understanding queue size distribution is critical for system performance evaluation.
  • Finite-capacity systems present unique challenges in analysis.

Purpose of the Study:

  • To develop and analyze a finite-capacity queueing model.
  • To derive the time-sensitive queue-size distribution.
  • To provide a computationally tractable solution for the model.

Main Methods:

  • Utilizing the embedded Markov chain paradigm.
  • Applying the total probability law.
  • Solving a system of equations for Laplace transforms using an algebraic approach.

Main Results:

  • Established a system of equations for the time-sensitive queue-size distribution.
  • Obtained a compact algebraic solution for the Laplace transforms.
  • Presented numerical illustrations of the model's performance.

Conclusions:

  • The proposed model and solution method are effective for finite-capacity queues with hyper-exponential service.
  • The algebraic approach offers a compact and efficient way to solve queueing models.
  • The findings have implications for optimizing systems with similar arrival and service characteristics.