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相关概念视频

Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
Noncompartmental Analysis: Statistical Moment Theory00:56

Noncompartmental Analysis: Statistical Moment Theory

Noncompartmental analyses leverage statistical moment theory to examine time-related changes in macroscopic events, encapsulating the collective outcomes stemming from the constituent elements in play. Statistical moment theory is a mathematical approach used to describe the time course of drug concentration in the body without assuming a specific compartmental model. SMT provides insights into drug absorption, distribution, metabolism, and elimination by treating drug concentration versus time...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time01:02

Noncompartmental Analysis: Mean Transit, Absorption and Dissolution Time

When drugs are administered extravascularly, a comprehensive evaluation through noncompartmental analysis becomes imperative. This analytical approach considers various parameters that play a crucial role in understanding the pharmacokinetics of these drugs.
One of the key parameters is the mean transit time (MTT), which refers to the total duration required for drug molecules to transit through the body. MTT is determined by calculating the ratio of the area under the moment curve to the area...

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相关实验视频

Updated: May 10, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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集装箱码头停留时间流程的数据:基于多个视角的数据集.

Hanung Nindito Prasetyo1,2, Riyanarto Sarno1, Dedy Rahman Wijaya2

  • 1Department of Informatics, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia.

Data in brief
|September 27, 2024
PubMed
概括

本研究引入了一个新的事件日志数据集,用于集装箱停留时间在集装箱码头. 这一数据集支持先进的过程采矿研究,包括异常检测和过程改进.

关键词:
集装箱 集装箱 集装箱停留时间数据集事件日志事件日志多个视角的多个视角.

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科学领域:

  • 过程科学 过程科学
  • 数据科学数据科学数据科学
  • 物流和供应链管理的物流和供应链管理.

背景情况:

  • 集装箱停留时间是港口运营中的关键指标.
  • 集装箱停留时间的有效管理对于全球贸易至关重要.
  • 现有的系统管理港口运营,但需要数据驱动的洞察力.

研究的目的:

  • 介绍一个新的事件日志数据集,重点关注集装箱停留时间过程.
  • 促进工艺科学方面的研究,特别是工艺采矿和异常检测.
  • 提供包括Case_ID,活动,时间,工人,等待时间和成本在内的全面数据集.

主要方法:

  • 在六个月内从集装箱码头信息系统收集数据.
  • 为居住时间过程提取和匿名化三个月的事件日志数据.
  • 用关键的过程挖掘变量和额外的指标构建数据集.

主要成果:

  • 现在可以获得对集装箱停留时间的精心策划的事件日志数据集.
  • 数据集是匿名的,并以道德为来源.
  • 包括超越标准过程挖矿变量的多视角指标.

结论:

  • 事件日志数据集对于工艺科学和工艺采矿的研究人员来说非常有价值.
  • 它使得开发过程发现,符合性检查和改进的新方法成为可能.
  • 促进对集装箱码头内的过程异常检测的研究.