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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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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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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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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.
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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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Harvesting and Disaggregation: An Overlooked Step in Biofilm Methods Research
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The SDN Approach for the Aggregation/Disaggregation of Sensor Data.

Yi-Bing Lin1, Shie-Yuan Wang2, Ching-Chun Huang3

  • 1Department of Computer Science, National Chiao Tung University, Hsinchu 300, Taiwan. liny@csie.nctu.edu.tw.

Sensors (Basel, Switzerland)
|June 27, 2018
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Summary
This summary is machine-generated.

This study introduces efficient packet aggregation and disaggregation for Internet of Things (IoT) applications using programmable Software Defined Networking (SDN) switches. P4 programs enable line-rate processing, significantly reducing network traffic and enhancing IoT data throughput.

Keywords:
Internet of ThingsP4Software Defined Networkingaggregationdisaggregationprogrammable switchsensor data

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Area of Science:

  • Computer Science
  • Network Engineering
  • Internet of Things

Background:

  • Internet of Things (IoT) applications generate numerous small data packets, leading to network congestion.
  • Current software-based packet aggregation/disaggregation methods at servers cause high latency and low throughput.
  • Efficiently managing high volumes of IoT data is crucial for scalable network performance.

Purpose of the Study:

  • To develop a novel approach for fast packet aggregation and disaggregation in IoT networks.
  • To leverage programmable Software Defined Networking (SDN) switches for efficient data handling.
  • To improve the throughput and reduce latency in IoT data transmission.

Main Methods:

  • Utilized Programming Protocol-Independent Packet Processor (P4) technology for switch programmability.
  • Designed and implemented custom P4 programs for packet aggregation and disaggregation.
  • Conducted experiments on commercial P4 switches at high bit rates (up to 100 Gbps).

Main Results:

  • Achieved line-rate packet aggregation without additional processing cost.
  • Demonstrated that packet disaggregation time is comparable to processing individual messages.
  • Implemented IoT message aggregation at a record 100 Gbps bit rate.
  • Proposed a buffer mechanism within P4 switches to reduce disaggregation processing power.

Conclusions:

  • Programmable P4 switches offer a viable solution for efficient IoT packet aggregation and disaggregation.
  • The proposed P4 programs significantly enhance IoT network throughput and reduce latency.
  • This approach overcomes the limitations of traditional software-based processing for high-volume IoT data.