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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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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.
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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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WoR+ Ontology: Modeling Data and Services in Web Connected Environments.

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Summary

The Web of Resources (WoR+) ontology enhances the Web of Things (WoT) by enabling seamless discovery, selection, and composition of IoT data and services through a unified vocabulary and reasoning capabilities.

Keywords:
Web connected environmentWeb resourcecompositiondataontologysemantic Web modelingservice

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Area of Science:

  • Computer Science
  • Semantic Web Technologies
  • Internet of Things

Background:

  • The Web of Things (WoT) standards by W3C aim for interoperability across diverse Internet of Things (IoT) platforms.
  • Effective resource description is crucial for syntactic and semantic interoperability in heterogeneous IoT environments.
  • Existing methods may lack a unified, dynamic knowledge representation for Web-connected resources.

Purpose of the Study:

  • To introduce WoR+, a novel ontology for describing Web resources within the Web of Things.
  • To provide a modular and unified vocabulary for representing Web services and Web data.
  • To enhance the discovery, selection, composition, and reasoning capabilities for IoT resources.

Main Methods:

  • Development of WoR+, a Web of Resources ontology.
  • Utilizing a modular and unified vocabulary for resource description.
  • Incorporating reasoning capabilities for knowledge inference.

Main Results:

  • WoR+ supports efficient discovery, selection, and composition of data and services.
  • The ontology enables inferring new knowledge through reasoning.
  • Experimental evaluation demonstrated high effectiveness, performance, clarity, and consistency of WoR+.

Conclusions:

  • WoR+ effectively addresses the need for a unified knowledge representation in the Web of Things.
  • The ontology facilitates enhanced interoperability and resource management in IoT ecosystems.
  • WoR+ is extensible and adaptable to evolving domain requirements.