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Related Concept Videos

Scale-Up Processes01:14

Scale-Up Processes

The scale-up of microbial fermentation processes is essential in industrial biotechnology, allowing the transition from laboratory-scale experiments to commercial-scale production while aiming to maintain product yield and quality. This process requires meticulous adjustment of equipment design, process parameters, and contamination control strategies to accommodate increasing culture volumes.At the laboratory scale, cultures are typically maintained in 1 to 10-liter glass or autoclavable...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each path...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

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Related Experiment Videos

Complex processes from dynamical architectures with time-scale hierarchy.

Dionysios Perdikis1, Raoul Huys, Viktor Jirsa

  • 1Theoretical Neuroscience Group, UMR6233 Institut Science du Mouvement, University of the Mediterranean, Marseille, France. dionysios.perdikis@etumel.univmed.fr

Plos One
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamical framework to understand complex behaviors by analyzing their constituent elementary processes. It reveals a tradeoff in internal and external influences, justifying efficient composition of complex actions.

Related Experiment Videos

Area of Science:

  • Dynamical systems theory
  • Computational neuroscience
  • Theoretical biology

Background:

  • Complex behaviors (motor, perceptual, cognitive) are thought to arise from smaller units.
  • A principled framework for defining these elementary processes and their relationships is lacking.
  • Current functional architectures are often piecemeal and instance-specific.

Purpose of the Study:

  • To develop a general dynamical framework for functional architectures.
  • To characterize architectures by the time-scale separation of their constituents.
  • To evaluate the efficiency of different functional architectures.

Main Methods:

  • Utilizing the concept of (phase) flow to describe system state evolution.
  • Classifying qualitatively distinct processes (functional modes) using phase flow topology.
  • Analyzing composite movements to illustrate architecture characterization based on time-scale separation.

Main Results:

  • A general dynamical framework for distinct functional architectures is presented.
  • Architectures are characterized by time-scale separation between internal functional modes and external influences.
  • A tradeoff between internal and external influences was identified.

Conclusions:

  • The proposed framework provides a principled way to define elementary processes and functional architectures.
  • Time-scale separation is a key characteristic for distinguishing and evaluating functional architectures.
  • The identified tradeoff offers theoretical justification for the efficient composition of complex behaviors from elementary processes.