Jove
Visualize
Contact Us

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
48
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
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...
36
What are Populations and Communities?00:30

What are Populations and Communities?

33.9K
Overview
33.9K
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

124
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
124
Censoring Survival Data01:09

Censoring Survival Data

78
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
78
McNemar's Test01:23

McNemar's Test

204
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
204

You might also read

Related Articles

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

Sort by
Same author

Transfer Irreversibilities in the Lenoir Cycle: FTT Design Criteria with ε-NTU.

Entropy (Basel, Switzerland)·2025
Same author

Particle Detection System Analysis in the Stratosphere Using High-Altitude Platforms Based on a MMPP-2 Model.

Sensors (Basel, Switzerland)·2025
Same author

Dual-Criteria Decision Analysis by Multiphotonic Effects in Nanostructured ZnO.

Micromachines·2024
Same author

Distance-Based Queuing for Scalable and Reliable Linear Wireless Sensor Networks in Smart Cities.

Sensors (Basel, Switzerland)·2024
Same author

Linear Irreversible Thermodynamics: A Glance at Thermoelectricity and the Biological Scaling Laws.

Entropy (Basel, Switzerland)·2023
Same author

Global Stability of the Curzon-Ahlborn Engine with a Working Substance That Satisfies the van der Waals Equation of State.

Entropy (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: Jun 23, 2025

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

8.7K

Performance Analysis of CSMA/NP under Finite Population Environments.

Ariadna I Rodriguez-Gomez1, Mario E Rivero-Angeles1, Izlian Y Orea-Flores1

  • 1Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City 07738, Mexico.

Sensors (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

This study analyzes the finite node CSMA Non-Persistent protocol, crucial for wireless sensor networks. Results show node count impacts throughput, aligning with infinite models at higher densities.

Keywords:
CSMAWSNsaccess protocolsfinite populationthroughput

More Related Videos

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

2.5K

Related Experiment Videos

Last Updated: Jun 23, 2025

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

8.7K
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.0K
A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes
09:10

A Highly Scalable Approach to Perform Ecological Surveys of Selfing Caenorhabditis Nematodes

Published on: March 1, 2022

2.5K

Area of Science:

  • Computer Science
  • Network Engineering
  • Performance Analysis

Background:

  • Traditional network analysis often uses infinite node models.
  • These models may not accurately represent systems with moderate node counts, like wireless sensor networks.
  • A finite node model offers more realistic dynamics for such applications.

Purpose of the Study:

  • To analyze the CSMA Non-Persistent protocol using a finite number of nodes.
  • To provide more accurate performance results for applications with moderate node counts.
  • To investigate the impact of node count on system throughput.

Main Methods:

  • Developed a finite node model for the CSMA Non-Persistent protocol.
  • Derived a complex closed-form throughput expression for a finite number of nodes.
  • Employed numerical methods to solve the derived expression.
  • Offered an approximate throughput expression for specific conditions.

Main Results:

  • System throughput is dependent on the finite node count.
  • At higher node counts, throughput behavior converges with predictions from Kleinrock's infinite model.
  • The finite model provides more accurate insights in low-contention scenarios where infinite models falter.

Conclusions:

  • The finite node model is essential for accurately analyzing CSMA Non-Persistent protocols in moderate-sized networks.
  • Understanding node count dependency is key to optimizing throughput in wireless sensor networks.
  • This research enhances the applicability of analytical models to real-world, finite network environments.