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

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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: 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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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Analysis of Population Pharmacokinetic Data01:12

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Sociohidrodinámica: Modelado basado en datos del comportamiento social

Daniel S Seara1, Jonathan Colen1,2,3, Michel Fruchart1,2,4

  • 1James Franck Institute, University of Chicago, Chicago, IL 60637.

Proceedings of the National Academy of Sciences of the United States of America
|August 29, 2025
PubMed
Resumen

Este estudio introduce un modelo sociohidrodinámico basado en datos para explicar la dinámica residencial. Revela la memoria social emergente y una explicación basada en la física para el fenómeno de las caídas de barrio.

Palabras clave:
sustancia activaLas economíasla hidrodinámicaAprendizaje automáticoSociología

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Área de la Ciencia:

  • Sistemas complejos
  • Sociofísica
  • Ciencias sociales computacionales

Sus antecedentes:

  • Los sistemas vivos exhiben comportamientos complejos influenciados por las fuerzas físicas y la toma de decisiones.
  • Las teorías hidrodinámicas ofrecen descripciones simplificadas de comportamientos colectivos, pero a menudo carecen de integración de datos.
  • Los modelos existentes para la dinámica social con frecuencia están desconectados de los datos empíricos.

Objetivo del estudio:

  • Desarrollar una tubería basada en datos que vincule el movimiento individual (micromotivas) con el comportamiento colectivo (macroconducta).
  • Construir y aplicar un modelo sociohidrodinámico para comprender la dinámica residencial en los Estados Unidos.
  • Evaluar sistemáticamente las hipótesis hidrodinámicas utilizando datos del mundo real.

Principales métodos:

  • Aumentando las teorías hidrodinámicas con las preferencias individuales para guiar el movimiento.
  • Utilizando una tubería basada en datos que integra datos del censo, encuestas sociológicas y análisis de redes neuronales.
  • Empleando inferencia estadística para calibrar un modelo sociohidrodinámico mínimo.

Principales resultados:

  • El modelo calibrado captura cualitativamente las características clave de la dinámica residencial de los Estados Unidos a nivel de condado.
  • Un efecto de memoria social, análogo a la histeresis magnética, surge durante las transiciones segregación-integración.
  • El modelo proporciona una analogía basada en la física para el cambio de vecindario, explicando los rápidos cambios demográficos.

Conclusiones:

  • Los modelos sociohidrodinámicos pueden describir efectivamente fenómenos sociales complejos como la segregación residencial.
  • El concepto de memoria social emergente ofrece nuevos conocimientos sobre la dinámica del comportamiento colectivo.
  • Este marco facilita el estudio de la motilidad guiada por la decisión en varios sistemas, desde microorganismos hasta poblaciones humanas.