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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

42
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...
42
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
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...
29
Quantitative Analysis01:12

Quantitative Analysis

249
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
249
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

60
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...
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相关实验视频

Updated: Jun 9, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
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Watershed Planning within a Quantitative Scenario Analysis Framework

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处理软变量和数据稀缺:从参与式系统动态建模过程中的量化中学到的经验教训

Irene Pluchinotta1, Ke Zhou1, Nici Zimmermann1

  • 1Institute for Environmental Design and Engineering, The Bartlett Faculty of The Built Environment, University College London, London, UK.

System dynamics review
|October 23, 2024
PubMed
概括

系统动态 (SD) 模型有助于与复杂的,无形因素的决策. 本研究解决了参与式SD过程中量化软变量和数据稀缺性的挑战.

科学领域:

  • 系统科学 系统科学
  • 决策科学 决策科学 决策科学
  • 参与式建模参与式建模

背景情况:

  • 系统动态 (SD) 模型对于复杂的问题结构和决策至关重要,特别是在数据有限和非线性关系的领域.
  • 在SD模型中量化无形或定性方面 (软变量) 是一个重大挑战,特别是当数据稀缺性阻止传统分析方法时.
  • 使用软变量参与方法获得和分析信息的现有程序是有限的.

研究的目的:

  • 审查软变量目前的量化方法,并确定开放问题,特别是关于数据稀缺的问题.
  • 详细说明在参与式系统动态框架内开发的量化过程,解决数据稀缺性和软变量.
  • 提出一个新的量化框架,适应不同的数据可用性和利益相关者的参与程度.

主要方法:

  • 对系统动态中的软变量现有量化技术的文献综述.
  • 在数据稀缺的情况下,开发和应用一种参与式方法来量化软变量.
  • 基于参与过程中的经验发现的框架设计.

主要成果:

  • 确定了当前用于量化软变量的方法中的差距,特别是在数据稀缺的情况下.
  • 在SD建模中展示了一种实际的,参与式的过程,以有效地处理软变量和数据限制.
  • 开发了一个灵活的量化框架,以数据的可用性和利益相关者的参与量化.

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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相关实验视频

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Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

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结论:

  • 解决系统动态中的软变量和数据稀缺问题需要创新的参与式方法.
  • 拟议的框架提供了一种结构化的方法,用于增强SD模型中定性元素的量化.
  • 有效的利益相关者参与对于在数据稀缺环境中成功量化至关重要.