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In computer science, a control-flow graph (CFG) is a representation, using graph notation, of all paths that might be traversed through a program during its execution. The control-flow graph was discovered by Frances E. Allen , [ 1 ] who noted that Reese T. Prosser used boolean connectivity matrices for flow analysis before.
Within an imperative programming language, a control flow statement is a statement that results in a choice being made as to which of two or more paths to follow. For non-strict functional languages, functions and language constructs exist to achieve the same result, but they are usually not termed control flow statements.
Control for a motor that allows more than one speed to be selected. advanced z-transform A mathematical technique used to model and analyze digital systems. affinity laws Mathematical formulas that relate the speed, flow, and diameter of pumps, fans, blowers, and turbines, useful for predicting output under varying conditions. agbioeletric
Passive sorting is done through control of the microfluidic channel design, allowing for discrimination based on droplet size. Size sorting relies on the bifurcating junctions in the channel to divert the flow, which causes droplets to sort based on how they interact with the cross section of that flow, the shear rate, which relates directly to ...
A turbine (/ ˈ t ɜːr b aɪ n / or / ˈ t ɜːr b ɪ n /) (from the Greek τύρβη, tyrbē, or Latin turbo, meaning vortex) [1] [2] is a rotary mechanical device that extracts energy from a fluid flow and converts it into useful work. The work produced can be used for generating electrical power when combined with a generator. [3]
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Flow-based programming defines applications using the metaphor of a "data factory". It views an application not as a single, sequential process, which starts at a point in time, and then does one thing at a time until it is finished, but as a network of asynchronous processes communicating by means of streams of structured data chunks, called "information packets" (IPs).
A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.