Sentence examples for response can be described from inspiring English sources

We also show that the response can be described by a properly defined effective temperature.

The complex baseband representation of the mobile wireless channel impulse response can be described by [13] (2).

The bifurcated flame response can be described simply from the effects that flow around the flame structure has on local velocities and scalar dissipation rates.

Many students exhibit a negative emotional reaction in response to mathematical tasks; this emotional response can be described as math anxiety [5].

The overall source-to-microphone response can be described as a convolution of the RIR (2) with the response of the corresponding directional microphone.

However, at the structural level, the macroscopic delamination response can be described effectively by concentrating all the sources of dissipation and non-linearity at the masonry-FRP interface, and assuming the support to behave as a linear-elastic body.

All of these responses can be described as theories of problem solving, whose ultimate goal is to make the generation of new ideas and theories more efficient.

Using multi-electrode recordings from cat visual cortex, we show that cortical responses can be described as trajectories in a high-dimensional pattern space.

These responses can be described as a typical model of inflammatory pain in which the sensory neurons are depolarized by directly activating a non-selective cationic channel of cutaneous, visceral and other types of peripheral afferent C fibers [ 24].

Coping responses can be described as positive or negative and as reactive (i.e. reacting to an individual's own thoughts and feelings) or active (dealing with actual stressful situations or events).

Assuming that neuronal responses can be described by Poisson statistics, we compute the log likelihood of s for the population response r i as following: log   L (s ) = ∑ i = 1 N log [ f i (s ) r i r i ! e x p (− f i (s ) ) ] = ∑ i = 1 N log (f i (s ) ) r i − ∑ i = 1 N f i (s ) − ∑ i = 1 N log (r i ! ) We can ignore the last term as it is stimulus s independent.