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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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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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Two-Compartment Open Model: Overview01:05

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Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...
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Two-Compartment Open Model: IV Bolus Administration01:18

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The two-compartment model for intravenous (IV) bolus administration illustrates drug distribution in the body, subdividing it into central and peripheral compartments. This model operates on the concept of two-compartment kinetics. The drug's plasma concentration shows a bi-exponential decline following IV bolus administration, signaling the presence of two disposition processes: distribution and elimination.
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Mechanistic Models: Overview of Compartment Models01:21

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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...
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Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

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Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
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Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

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The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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A Biobjective Stochastic Model for Intermodal Supply Chains: Application to the Corn and Soybean Flows.

Marco Marto1, Valentina Chkoniya2,3, Eduardo B Couto4

  • 1Aveiro Institute of Accounting and Administration and CIDMA Center for Research & Development in Mathematics and Applications, University of Aveiro 3810-500 Aveiro, Portugal.

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Summary

This study optimizes soybean and corn supply chain networks in Europe and North Africa. It identifies strategic port locations to minimize costs and emissions amid demand uncertainty.

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Area of Science:

  • Supply Chain Management
  • Operations Research
  • Agricultural Economics

Background:

  • Recent global disruptions (geopolitical, economic, health, weather) underscore the need for resilient supply chain networks (SCNs).
  • Effective planning and management of SCNs are crucial at all decision-making levels to mitigate impacts on businesses and populations.
  • Intermodal transportation and distribution networks for agricultural commodities like soybean and corn face significant demand and cost uncertainties.

Purpose of the Study:

  • To analyze and optimize soybean and corn supply chain networks (SCNs) across Europe and North Africa.
  • To incorporate demand and cost uncertainties into SCN design using stochastic modeling.
  • To redefine optimization goals considering expected cost (EC) and conditional value at risk (CVAR), alongside CO2 emissions.

Main Methods:

  • Developed a deterministic model as a baseline for SCN establishment.
  • Introduced uncertainty through stochastic modeling, adapting optimization objectives.
  • Employed a bi-objective optimization model to balance economic costs and environmental emissions (CO2).

Main Results:

  • Identified the strategic importance of the port of Itaqui on the supply side.
  • Highlighted the port of Sines as a key distribution hub (transshipment point) for efficient SCN design.
  • Demonstrated the advantageous role of the port of Sines in creating efficient intermodal SCNs for soybean and corn distribution.

Conclusions:

  • The strategic positioning of specific ports, such as Itaqui and Sines, is critical for designing robust and efficient intermodal SCNs.
  • Stochastic modeling effectively addresses uncertainties in demand and costs, leading to optimized SCNs.
  • Balancing economic objectives (cost minimization) with environmental considerations (CO2 emissions) is essential for sustainable agricultural commodity distribution.