Jove
Visualize
联系我们

相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
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...
57
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

527
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...
527
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

44
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...
44
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

74
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
74
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

151
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
151
Three-Compartment Open Model01:06

Three-Compartment Open Model

241
The three-compartment open model is a pharmacokinetic model used to describe the distribution and elimination of drugs following extravascular administration. It comprises a central compartment representing the plasma and two peripheral compartments. The highly perfused peripheral compartment represents organs and tissues with a rich blood supply, such as the liver, kidneys, and lungs. The scarcely perfused peripheral compartment represents tissues with lower blood supply, such as adipose...
241

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Toward online dairy milk fouling detection: A review highlighting critical gaps and future sensing insights.

Journal of dairy science·2026
Same author

Magnesite-modified seaweed biochar-chitosan hydrogel beads for phosphate removal: adsorption mechanism, interpretable machine learning and life cycle assessment.

Bioresource technology·2026
Same author

Revisiting China's domestic greenhouse gas emission from wastewater treatment: A quantitative process life-cycle assessment.

The Science of the total environment·2023
Same author

Comparison of struvite and K-struvite for Pb and Cr immobilisation in contaminated soil.

Journal of environmental management·2022
Same author

Non-permanent primary food packaging materials assessment: Identification, migration, toxicity, and consumption of substances.

Comprehensive reviews in food science and food safety·2022
Same author

Effect of saline water ionic strength on phosphorus recovery from synthetic swine wastewater.

Journal of environmental sciences (China)·2021
Same journal

A robust ATUB-Net for bearing fault diagnosis under unbalanced sample scenarios.

ISA transactions·2026
Same journal

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

ISA transactions·2026
Same journal

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

ISA transactions·2026
Same journal

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

ISA transactions·2026
Same journal

Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in wind tunnel systems.

ISA transactions·2026
Same journal

Enhancing interpretable soft sensing with embedded hybrid modeling: the GraphTrans approach for industrial processes.

ISA transactions·2026
查看所有相关文章
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

Updated: Jul 11, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

935

封面:一种无类的方法来生成平衡的数据集,用于过程建模.

Isaac Severinsen1, Wei Yu1, Timothy Walmsley2

  • 1Department of Chemical and Materials Engineering, University of Auckland, 5 Grafton Road, Auckland, 1010, New Zealand.

ISA transactions
|November 11, 2023
PubMed
概括
此摘要是机器生成的。

一种名为Covert (无类超采样技术) 的新方法可以改进工业数据集,以实现更好的流程建模. 它的性能优于Smote等现有方法,在工业数字双胞胎中,数据驱动模型的准确性提高了20%.

关键词:
无类过量抽样无类过量抽样数字双胞胎 数字双胞胎 数字双胞胎历史数据 历史数据不平衡的数据不平衡的数据不平衡回归是一种不平衡的回归.核密度估计的核密度估计.过程建模过程建模

更多相关视频

High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.8K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

相关实验视频

Last Updated: Jul 11, 2025

Surrogate Model Development for Digital Experiments in Welding
09:17

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

935
High-Throughput Metabolic Profiling for Model Refinements of Microalgae
11:07

High-Throughput Metabolic Profiling for Model Refinements of Microalgae

Published on: December 4, 2021

3.8K
A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.1K

科学领域:

  • 数据科学是数据科学.
  • 机器学习是机器学习.
  • 工业过程建模 工业过程建模

背景情况:

  • 开发可靠的工业流程模型对于数字双胞胎至关重要.
  • 历史数据集经常存在不平衡,限制了模型的适用性.
  • 现有的过量采样技术可能无法充分应对工业数据的挑战.

研究的目的:

  • 介绍Covert,一种用于不平衡的工业数据集的新型无类超采样技术.
  • 提高工业过程模拟数据驱动模型的性能.
  • 通过使用历史数据来提高过程模型的应用范围.

主要方法:

  • 隐藏利用内核密度估计和最近邻近算法来识别和重新采样稀疏的数据区域.
  • 该技术的重点是创建一个更平衡的数据集,以改善模型培训.
  • 对合成少数人过量采样技术 (Smote) 进行了比较分析.

主要成果:

  • 在统一填充输入特征空间方面,Covert在Smote上表现出优越的性能.
  • 与Smote.com相比,该技术在输出变量中产生了更可信的数据.
  • 使用Covert开发的数据驱动模型显示,在预测原始数据的特征空间之外时,准确性增加了20%.
  • 在相同的预测空间中,Smote导致模型准确度下降6%.

结论:

  • 隐蔽是一种有效的无类超采样技术,用于改善不平衡的工业数据集.
  • 该方法提高了数据驱动过程模型的准确性和预测范围.
  • 通过有效利用历史数据,Covert促进了更强大的数字双胞胎的开发.