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  2. Piping and plumbing fitting - Wikipedia

    en.wikipedia.org/wiki/Piping_and_plumbing_fitting

    Tees can connect pipes of different diameters, change the direction of a pipe run, or both. Available in various materials, sizes and finishes, they may also be used to transport two-fluid mixtures. [further explanation needed] Tees may be equal or unequal in size of their three connections, with equal tees the most common.

  3. Double tee - Wikipedia

    en.wikipedia.org/wiki/Double_tee

    Diagram of double tee beam. A double tee or double-T beam is a load-bearing structure that resembles two T-beams connected to each other side by side. The strong bond of the flange (horizontal section) and the two webs (vertical members, also known as stems) creates a structure that is capable of withstanding high loads while having a long span.

  4. Dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Dimensionality_reduction

    Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension.

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    www.aol.com/16-national-popcorn-day-deals...

    Moviegoers can visit AMC Theatres on Jan. 19 to score unlimited refills on any size popcorn purchase and 50% off the chain’s Loungefly popcorn bag with the purchase of any size popcorn.

  6. Piping - Wikipedia

    en.wikipedia.org/wiki/Piping

    Process piping and power piping are typically checked by pipe stress engineers to verify that the routing, nozzle loads, hangers, and supports are properly placed and selected such that allowable pipe stress is not exceeded under different loads such as sustained loads, operating loads, pressure testing loads, etc., as stipulated by the ASME B31, EN 13480, GOST 32388, RD 10-249 or any other ...

  7. Nonlinear dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_dimensionality...

    It also presents a challenge for humans, since it's hard to visualize or understand data in more than three dimensions. Reducing the dimensionality of a data set, while keep its essential features relatively intact, can make algorithms more efficient and allow analysts to visualize trends and patterns.