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相关概念视频

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

160
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
160
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

252
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,...
252
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
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...
100
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
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...
86
Three-Compartment Open Model01:06

Three-Compartment Open Model

422
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...
422
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

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无边界4D城市的组合生成模型

Haozhe Xie, Zhaoxi Chen, Fangzhou Hong

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    此摘要是机器生成的。

    通过将动态流量与静态场景分开,并使用专门的神经场所构建对象, CityDreamer4D可以生成无限的4D城市. 这种组合方法可以实现现实的4D城市生成,并支持城市模拟等应用.

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    科学领域:

    • 计算机视觉
    • 人工智能
    • 3D场景生成

    背景情况:

    • 三维场景的生成正在迅速发展.
    • 由于复杂的物体和人类对城市扭曲的敏感性,产生动态的4D城市带来了独特的挑战.

    研究的目的:

    • 提出城市梦想者4D,一个无限制的4D城市生成的构成模型.
    • 通过分离动态和静态元素并利用多样化的神经场所来解决4D城市的复杂性.

    主要方法:

    • 城市梦想者4D将动态物体 (车辆) 与静态场景 (建筑物,道路) 分开.
    • 它使用组合神经场 (面向物体和面向实例) 与定制的生成哈希网和周期性的位置嵌入.
    • 使用交通场景生成器和无限布局生成器,具有紧的鸟视图 (BEV) 表示.

    主要成果:

    • 在创建现实的4D城市方面展示了最先进的性能.
    • 成功生成动态交通场景和静态城市布局.
    • 提供全面的数据集 (OSM,谷歌地球,CityTopia) 用于4D城市生成研究.

    结论:

    • 城市梦想家4D的构造设计有效地应对了4D城市的挑战.
    • 该模型支持多种下游应用,包括实例编辑,城市风格化和城市模拟.
    • 推进复杂,动态城市环境的生成模型领域.