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Despite the great potential complexity and diversity of biological networks, all first-order network behavior generalizes to one of four possible input-output motifs: hyperbolic or Michaelis–Menten, ultra-sensitive, bistable, and bistable irreversible (a bistability where negative and therefore biologically impossible input is needed to return from a state of high output).
Here represents the square matrix of input coefficients, denotes releases (such as emissions or waste) per unit of output or the intervention matrix, stands for the vector of final demand (or functional unit), is the identity matrix, and represents the resulting releases (For further details, refer to the input-output model).
Open systems have input and output flows, representing exchanges of matter, energy or information with its surroundings. An open system is a system that has external interactions. Such interactions can take the form of information, energy, or material transfers into or out of the system boundary, depending on the discipline which defines the ...
Simultaneous Inversion (SI) is a pre-stack method that uses multiple offset or angle seismic sub-stacks and their associated wavelets as input; it generates P-impedance, S-impedance and density as outputs (although the density output resolution is rarely as high as the impedances). This helps improve discrimination between lithology, porosity ...
Definition of a three-dimensional mesh honoring the structural model to support volumetric representation of heterogeneity (see Geostatistics) and solving the Partial Differential Equations which govern physical processes in the subsurface (e.g. seismic wave propagation, fluid transport in porous media).
ISS unified the Lyapunov and input-output stability theories and revolutionized our view on stabilization of nonlinear systems, design of robust nonlinear observers, stability of nonlinear interconnected control systems, nonlinear detectability theory, and supervisory adaptive control. This made ISS the dominating stability paradigm in ...
where y i is the output of the i th neuron, x j is the j th input neuron signal, w ij is the synaptic weight (or strength of connection) between the neurons i and j, and φ is the activation function. While this model has seen success in machine-learning applications, it is a poor model for real (biological) neurons, because it lacks time ...
where I and O are the input and output rates. In the above example, the steady-state input and output rates are both equal to a, so τ res = 1/k. [20] If the input and output rates are nonlinear functions of C, they may still be closely balanced over time scales much greater than the residence time; otherwise, there will be large fluctuations in C.