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The former ground link of the fusing 4-bar linkage becomes a rectilinear link that travels follows the same coupler curve. Each of these paired six-bar cognate linkages can also be converted into another cognate linkage by flipping the linkage over, and switching the roles of the rectilinear link and the ground link.
The linkage was first shown in Paris on the Exposition Universelle (1878) as "The Plantigrade Machine". [5] [3] The Chebyshev Lambda Linkage is a cognate linkage of the Chebyshev linkage. The Chebyshev Lambda Linkage is used in vehicle suspension mechanisms, walking robots, and rover wheel mechanisms.
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The Scott Russell linkage (1803) translates linear motion through a right angle, but is not a straight line mechanism in itself. The Grasshopper beam/Evans linkage, an approximate straight line linkage, and the Bricard linkage, an exact straight line linkage, share similarities with the Scott Russell linkage and the Trammel of Archimedes.
A common exception is names of publications, and publishers named for them, e.g.: The New York Times, The New York Times Company. In some cases, leading articles (usually The) are an integral part of the company name (as determined by usage in independent reliable sources) and should be included, especially when necessary for disambiguation, e.g.:
If we distinguish folding quadrilateral linkage, then there are 27 different cases. [3] The figure shows examples of the various cases for a planar quadrilateral linkage. [4] Types of four-bar linkages, s: shortest link, l: longest link. The configuration of a quadrilateral linkage may be classified into three types: convex, concave, and crossing.
This list contains Germanic elements of the English language which have a close corresponding Latinate form. The correspondence is semantic—in most cases these words are not cognates, but in some cases they are doublets, i.e., ultimately derived from the same root, generally Proto-Indo-European, as in cow and beef, both ultimately from PIE *gʷōus.
Named-entity recognition (NER) locates and classifies named entities in unstructured text into pre-defined categories such as names, organizations, locations, and more. For example, the following sentence: Paris is the capital of France. would be processed by an NER system to obtain the following output: [Paris] City is the capital of [France ...