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Finger-jointed lumber – solid dimensional lumber lengths typically are limited to lengths of 22 to 24 feet (6.7–7.3 m), but can be made longer by the technique of "finger-jointing" by using small solid pieces, usually 18 to 24 inches (460–610 mm) long, and joining them together using finger joints and glue to produce lengths that can be ...
Since the -form gauge fields naturally couple to extended objects with dimensional world-volume, Type IIA string theory naturally incorporates various extended objects like D0, D2, D4 and D6 branes (using Hodge duality) among the D-branes (which are charged) and F1 string and NS5 brane among other objects.
In 1971 "Micro=Lam LVL" was introduced. "Micro=Lam LVL" consisted of laminated veneer lumber billets 4 feet (1.2 m) wide, 3 + 1 ⁄ 2 inches (89 mm) thick, and 80 feet (24 m) long. Troutner proved the structural capabilities of his Micro=Lam product by building a house in Hagerman, Idaho, using beams made of Micro=Lam.
The AASHTO Soil Classification System was developed by the American Association of State Highway and Transportation Officials, and is used as a guide for the classification of soils and soil-aggregate mixtures for highway construction purposes.
The Whitney trick requires 2+1 dimensions (2 space, 1 time), hence the two Whitney disks of surgery theory require 2+2+1=5 dimensions. The reason for dimension 5 is that the Whitney trick works in the middle dimension in dimension 5 and more: two Whitney disks generically don't intersect in dimension 5 and above, by general position ( 2 + 2 < 5 ...
A simple application of dimensional analysis to mathematics is in computing the form of the volume of an n-ball (the solid ball in n dimensions), or the area of its surface, the n-sphere: being an n-dimensional figure, the volume scales as x n, while the surface area, being (n − 1)-dimensional, scales as x n−1.
The online learning algorithms, on the other hand, incrementally build their models in sequential iterations. In iteration t, an online algorithm receives a sample, x t and predicts its label ลท t using the current model; the algorithm then receives y t, the true label of x t and updates its model based on the sample-label pair: (x t, y t).
In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). [1]