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Passive Diffusion: Overview and Kinetics01:17

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Passive diffusion is a critical process that allows small lipophilic drugs to cross the cell membrane along a concentration gradient. This mechanism's efficiency depends on four primary factors: the membrane's surface area, the drug's lipid-water partition coefficient, the concentration gradient, and the membrane's thickness.
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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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An acid-base reaction is one in which a hydrogen ion, H+, is transferred from one chemical species to another. Such reactions are of central importance to numerous natural and technological processes, ranging from the chemical transformations within cells or lakes and oceans to the industrial-scale production of fertilizers, pharmaceuticals, and other substances essential to the society.
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Acids and bases play several important roles in biology. The pH of a biological system can significantly impact the function of biological molecules, including enzymes, proteins, and nucleic acids. For example, enzymes have optimal pH ranges for their activity, and changes in pH can denature or alter their structure, affecting their function. Acids and bases also play a crucial role in cellular signaling and communication. The pH of the extracellular fluid around cells can influence the...
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Passivity analysis of delayed reaction-diffusion memristor-based neural networks.

Yanyi Cao1, Yuting Cao1, Shiping Wen1

  • 1School of Automation, Huazhong University of Science and Technology, Wuhan, PR China.

Neural Networks : the Official Journal of the International Neural Network Society
|November 17, 2018
PubMed
Summary

This study establishes conditions for passivity in delayed reaction-diffusion memristor-based neural networks (RDMNNs). These findings, verifiable via linear matrix inequalities (LMIs), ensure network stability and enable applications like pseudo-random number generation.

Keywords:
MemristorNeural networkPassivity

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Area of Science:

  • Neural Networks
  • Control Theory
  • Nonlinear Systems

Background:

  • Memristor-based neural networks (MNNs) offer advanced computational capabilities.
  • Delayed systems introduce complexities in stability analysis.
  • Passivity is a crucial property for analyzing stability and designing controllers for dynamical systems.

Purpose of the Study:

  • To investigate and establish sufficient conditions for the passivity of delayed reaction-diffusion memristor-based neural networks (RDMNNs).
  • To extend the analysis to RDMNNs without time delays.
  • To demonstrate the practical applicability of the derived conditions and RDMNNs in areas like pseudo-random number generation.

Main Methods:

  • Utilizing inequality techniques to derive stability conditions.
  • Constructing appropriate Lyapunov functionals for system analysis.
  • Formulating conditions in the form of linear matrix inequalities (LMIs) for ease of verification.

Main Results:

  • Sufficient conditions for passivity, output strict passivity, and input strict passivity of delayed RDMNNs were derived.
  • The derived conditions are presented as LMIs, readily verifiable using computational tools like the Matlab toolbox.
  • The passivity of RDMNNs without delays was also addressed.

Conclusions:

  • The theoretical results provide a robust framework for analyzing the passivity of delayed RDMNNs.
  • The LMIs offer a computationally efficient method for verifying network passivity.
  • The study highlights the potential of RDMNNs in advanced applications, including secure pseudo-random number generation.