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

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Reaction Mechanisms: The Steady-State Approximation01:26

Reaction Mechanisms: The Steady-State Approximation

The steady-state approximation, also referred to as the quasi-steady-state approximation to differentiate it from a true steady state, is a widely used method for simplifying calculations in complex reaction mechanisms. This approach is particularly useful when dealing with multi-step reactions that involve reverse reactions or several steps, which can significantly increase mathematical complexity and make the reactions nearly unsolvable analytically.The steady-state approximation operates on...
Multimachine Stability01:25

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Reaction Mechanisms: Rate-limiting Step Approximation01:29

Reaction Mechanisms: Rate-limiting Step Approximation

The rate-determining step, or RDS, in a chemical reaction is the slowest step that determines the overall reaction rate. It is identified by using the observed rate law and typically involves approximation methods like the RDS approximation or the steady-state approximation.In the RDS approximation, also known as the rate-limiting-step or equilibrium approximation, the reaction mechanism consists of one or more reversible reactions near equilibrium, followed by a slower RDS, and then one or...

You might also read

Related Articles

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

Sort by
Same author

Longitudinal transcriptomic profiling identifies predictors of response to neoadjuvant chemoimmunotherapy in triple-negative breast cancer: results from the NeoTRIPaPDL1 trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2026
Same author

Reply to Letter to the Editor "The curious incident of the T-DXd alone arm killed at (DESTINY-Breast) eleven o'clock" by Orlandi.

Annals of oncology : official journal of the European Society for Medical Oncology·2026
Same author

Impact of anthracyclines in genomic high-risk, node-negative, HR-positive/HER2-negative breast cancer.

Annals of oncology : official journal of the European Society for Medical Oncology·2025
Same author

Immune-related adverse events are associated with better event-free survival in a phase I/II clinical trial of durvalumab concomitant with neoadjuvant chemotherapy in early-stage triple-negative breast cancer.

ESMO open·2025
Same author

Pathologic complete response (pCR) rates for patients with HR+/HER2- high-risk, early-stage breast cancer (EBC) by clinical and molecular features in the phase II I-SPY2 clinical trial.

Annals of oncology : official journal of the European Society for Medical Oncology·2024
Same author

Event-free survival by residual cancer burden with pembrolizumab in early-stage TNBC: exploratory analysis from KEYNOTE-522.

Annals of oncology : official journal of the European Society for Medical Oncology·2024

Related Experiment Video

Updated: May 31, 2026

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
07:31

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Development of the time-dependent reverse Monte Carlo simulation, RMCt.

O Gereben1, L Pusztai, R L McGreevy

  • 1Ardeus Ltd, H-2030 Érd, Fenyofa ucta 32, Hungary.

Journal of Physics. Condensed Matter : an Institute of Physics Journal
|June 23, 2011
PubMed
Summary

A new time-dependent reverse Monte Carlo modelling (RMCt) method simulates material atomic dynamics. This approach utilizes inelastic neutron scattering data for advanced material analysis.

More Related Videos

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches
05:56

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Related Experiment Videos

Last Updated: May 31, 2026

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies
07:31

Realistic Membrane Modeling Using Complex Lipid Mixtures in Simulation Studies

Published on: September 1, 2023

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
09:17

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches
05:56

Exploring Caspase Mutations and Post-Translational Modification by Molecular Modeling Approaches

Published on: October 13, 2022

Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Computational Chemistry

Background:

  • Understanding atomic dynamics is crucial for material properties.
  • Existing modeling techniques may not fully capture time-dependent behavior.
  • Inelastic neutron scattering provides valuable dynamic information.

Purpose of the Study:

  • To develop a novel time-dependent reverse Monte Carlo modelling (RMCt) method.
  • To enable the modeling of atomic dynamics in materials.
  • To leverage inelastic neutron scattering data for dynamic material simulations.

Main Methods:

  • Development of the time-dependent reverse Monte Carlo (RMCt) algorithm.
  • Utilizing dynamic pair correlation function, g(r,t), as input.
  • Employing dynamic structure factor, S(Q,ω), from neutron scattering experiments.

Main Results:

  • A functional RMCt method for simulating atomic dynamics.
  • Successful modeling of material dynamics using experimental scattering data.
  • Potential for accurate representation of g(r,t) and S(Q,ω).

Conclusions:

  • The RMCt method offers a powerful tool for studying material dynamics.
  • This approach enhances the analysis of inelastic neutron scattering data.
  • Provides new insights into the time-dependent behavior of materials.