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Related Concept Videos

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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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...
427
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

614
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,...
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Three-Compartment Open Model01:06

Three-Compartment Open Model

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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Related Experiment Videos

Logistic Model to Support Service Modularity for the Promotion of Reusability in a Web Objects-Enabled IoT

Muhammad Golam Kibria1, Sajjad Ali2, Muhammad Aslam Jarwar3

  • 1Department of CICE, Hankuk University of Foreign Studies, 02450 Seoul, Korea. kibria@hufs.ac.kr.

Sensors (Basel, Switzerland)
|September 23, 2017
PubMed
Summary
This summary is machine-generated.

To manage the vast number of virtual objects in the Internet of Things (IoT), this study promotes service modularity. Reusing virtual objects and composite virtual objects through similarity matching reduces exponential growth and enhances efficiency.

Keywords:
Internet of Things (IoT)Web of Objects (WoO)object virtualizationreusabilitysemantic ontology

Related Experiment Videos

Area of Science:

  • Computer Science
  • Internet of Things (IoT)
  • Software Engineering

Background:

  • The Internet of Things (IoT) environment faces exponential growth in virtual objects due to numerous connected devices.
  • Creating new virtual objects for each service request leads to inefficiency and scalability issues.
  • Existing virtual objects and composite virtual objects require a framework for effective reuse.

Purpose of the Study:

  • To introduce and explore the concept of service modularity for virtual objects in the Web Objects-Enabled IoT.
  • To propose a logistic model that supports service modularity and promotes reusability.
  • To discuss similarity matching algorithms for virtual objects and composite virtual objects.

Main Methods:

  • Application of the service modularity principle to virtual objects and composite virtual objects.
  • Development of a logistic model to facilitate the reuse of virtual objects.
  • Discussion of functional components and a flowchart for reusing composite virtual objects.
  • Implementation of a use case scenario to demonstrate service modularity.

Main Results:

  • Demonstrated that reusing existing virtual objects and composite virtual objects avoids duplication.
  • Showcased reduced time for searching and instantiating virtual objects through similarity matching.
  • Validated the proposed logistic model's support for service modularity in the Web Objects-Enabled IoT.

Conclusions:

  • Service modularity is crucial for managing the complexity of virtual objects in IoT environments.
  • The proposed logistic model effectively promotes the reusability of virtual objects and composite virtual objects.
  • Similarity matching is a key enabler for efficient reuse, enhancing the scalability of IoT services.