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A sieve analysis (or gradation test) is a practice or procedure used in geology, civil engineering, [1] and chemical engineering [2] to assess the particle size distribution (also called gradation) of a granular material by allowing the material to pass through a series of sieves of progressively smaller mesh size and weighing the amount of material that is stopped by each sieve as a fraction ...
The Goldston–Pintz–Yıldırım sieve (also called GPY sieve or GPY method) is a sieve method and variant of the Selberg sieve with generalized, multidimensional sieve weights. The sieve led to a series of important breakthroughs in analytic number theory. It is named after the mathematicians Dan Goldston, János Pintz and Cem Yıldırım. [1]
Soil gradation is determined by analyzing the results of a sieve analysis or a hydrometer analysis. [4] [5] In a sieve analysis, a coarse-grained soil sample is shaken through a series of woven-wire square-mesh sieves. Each sieve has successively smaller openings so particles larger than the size of each sieve are retained on the sieve.
In granulometry, the particle-size distribution (PSD) of a powder, or granular material, or particles dispersed in fluid, is a list of values or a mathematical function that defines the relative amount, typically by mass, of particles present according to size. [1]
Although such information contains long lists of sieve sizes, in practice sieves are normally used in series in which each member sieve is selected to pass particles approximately 1/ √ 2 smaller in diameter or 1/2 smaller in cross-sectional area than the previous sieve. For example the series 80mm, 63, 40, 31.5, 20, 16, 14, 10, 8, 6.3, 4, 2.8 ...
Wentworth grain size chart from United States Geological Survey Open-File Report 2006-1195: Note size typos; 33.1mm is 38.1 & .545mm is .594 Beach cobbles at Nash Point, South Wales
The problem is to estimate separately the number of numbers ≤ x with no prime divisors ≤ x 1/2, that have an even (or an odd) number of prime factors. It can be shown that, no matter what the choice of weights in a Brun- or Selberg-type sieve, the upper bound obtained will be at least (2 + o(1)) x / ln x for both problems. But in fact the ...
Sieve estimators have been used extensively for estimating density functions in high-dimensional spaces such as in Positron emission tomography (PET). The first exploitation of Sieves in PET for solving the maximum-likelihood image reconstruction problem was by Donald Snyder and Michael Miller, [1] where they stabilized the time-of-flight PET problem originally solved by Shepp and Vardi. [2]