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The concepts of fluid intelligence (g f) and crystallized intelligence (g c) were introduced in 1943 by the psychologist Raymond Cattell. [ 1 ] [ 2 ] [ 3 ] According to Cattell's psychometrically -based theory, general intelligence ( g ) is subdivided into g f and g c .
The distributions of a wide variety of physical, biological, and human-made phenomena approximately follow a power law over a wide range of magnitudes: these include the sizes of craters on the moon and of solar flares, [2] cloud sizes, [3] the foraging pattern of various species, [4] the sizes of activity patterns of neuronal populations, [5] the frequencies of words in most languages ...
A Newtonian fluid is a power-law fluid with a behaviour index of 1, where the shear stress is directly proportional to the shear rate: = These fluids have a constant viscosity, μ, across all shear rates and include many of the most common fluids, such as water, most aqueous solutions, oils, corn syrup, glycerine, air and other gases.
While no real fluid fits the definition perfectly, many common liquids and gases, such as water and air, can be assumed to be Newtonian for practical calculations under ordinary conditions. However, non-Newtonian fluids are relatively common and include oobleck (which becomes stiffer when vigorously sheared) and non-drip paint (which becomes ...
Now Fluid Concepts and Creative Analogies presents that model, along with the computer programs Hofstadter and his associates have designed to test it. These programs work in stripped-down yet surprisingly rich microdomains. On April 3, 1995, Fluid Concepts and Creative Analogies became the first book ordered online by an Amazon.com customer. [3]
Dimensionless numbers (or characteristic numbers) have an important role in analyzing the behavior of fluids and their flow as well as in other transport phenomena. [1] They include the Reynolds and the Mach numbers, which describe as ratios the relative magnitude of fluid and physical system characteristics, such as density, viscosity, speed of sound, and flow speed.
Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).
Estimating the power-law exponent of a scale-free network is typically done by using the maximum likelihood estimation with the degrees of a few uniformly sampled nodes. [14] However, since uniform sampling does not obtain enough samples from the important heavy-tail of the power law degree distribution, this method can yield a large bias and a ...