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

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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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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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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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Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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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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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Logistics Finance Collaborative Development Model Based on Machine Learning.

Yuqin Wang1

  • 1Department of Logistics Management, Xi'an International University, Xi'an 710077, China.

Computational Intelligence and Neuroscience
|October 14, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning approach to enhance logistics financial models, mitigating credit risks and improving efficiency. The new model optimizes vehicle paths, maximizing economic value in transportation.

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Area of Science:

  • Logistics and Supply Chain Management
  • Financial Technology (FinTech)
  • Machine Learning Applications

Background:

  • Logistics finance models are crucial for small and medium-sized enterprises but face credit risks due to information uncertainty and pursuit of high short-term returns.
  • Integrating machine learning with logistics finance requires collaboration and robust information platforms to overcome inherent challenges.
  • Traditional neural network algorithms in logistics finance have limitations that hinder optimal performance and risk management.

Purpose of the Study:

  • To analyze and model the collaborative development of logistics finance using machine learning.
  • To address overfitting issues in model building caused by limited sample features.
  • To propose a novel neural network-based model for non-complete vehicle path optimization in logistics finance.

Main Methods:

  • Machine learning techniques were employed to analyze logistics financial data and construct sample characteristics.
  • A new feature selection method combining Pearson correlation coefficient and Principal Component Analysis (PCA) was designed to mitigate overfitting.
  • An integrated learning method and a neural network-based non-complete vehicle path optimization model were developed, incorporating time domain length and spatial probability.

Main Results:

  • The proposed feature selection method effectively addresses overfitting in machine learning models for logistics finance.
  • The novel neural network model significantly improves logistics efficiency by optimizing vehicle paths.
  • Simulation results demonstrate the maximization of economic value within the transportation process.

Conclusions:

  • The integration of advanced machine learning, specifically improved neural networks and feature selection, offers a robust solution for logistics financial challenges.
  • The developed model enhances operational efficiency and economic returns in logistics finance, addressing critical credit risk factors.
  • This research paves the way for more secure and profitable logistics financial services through technological innovation.