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En esta página puede obtener un análisis detallado de una palabra o frase, producido utilizando la mejor tecnología de inteligencia artificial hasta la fecha:
- cómo se usa la palabra
- frecuencia de uso
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- ejemplos de uso (varias frases con traducción)
- etimología
Qué (quién) es lexical nesting depth - definición
CLASS OF COMPUTIONAL PACKING OPTIMISATION PROCEDURE
Nesting algorithms
Teufe
DEPTH OF A BOREHOLE OR MINE SHAFT RELATED TO THE SURFACE
Depth (mining)
Teufe is the German mining term for depth. The Teufe (hT) indicates how deep a given point lies below the surface of an open-cast pit or below the ground level in the area surrounding the pit. By contrast, the height, h, refers to its distance from a reference surface 'above'. The vertical distance between the surface and a point in the mine (Grubengebäude), i.e. the vertical depth, was formerly also called the Seigerteufe. This difference is no longer made today. The terms Teufe and Seigerteufe are synonymous.
Lexical hypothesis
![[[Gordon Allport]]](https://commons.wikimedia.org/wiki/Special:FilePath/Gordon Allport.gif?width=200 "[[Gordon Allport]]")
HYPOTHESIS IN PERSONALITY PSYCHOLOGY THAT PERSONALITY TRAITS IMPORTANT TO A GROUP BECOME A PART OF THAT GROUP’S LANGUAGE
Sedimentation hypothesis; Fundamental lexical hypothesis; Lexical Hypothesis; Psycholexical
The lexical hypothesis (also known as the fundamental lexical hypothesis, lexical approach, or sedimentation hypothesis) is a thesis, current primarily in early personality psychology, and subsequently subsumed by many later efforts in that subfield. Despite some variation in its definition and application, the hypothesis is generally defined by two postulates.
Nesting (computing)
COMPUTING SCIENCE AND INFORMATICS TERM FOR INFORMATION ORGANIZED IN LAYERS OR WITH OBJECTS WITHIN OBJECTS, FOR EXAMPLE RECURSIVELY
Nesting (computers); Nesting (programming)
In computing science and informatics, nestinghttps://study.com/academy/lesson/nesting-loops-statements-in-c-programming.
Wikipedia
Nesting algorithm
Nesting algorithms are used to make the most efficient use of material or space by evaluating many different possible combinations via recursion.
- Linear (1-dimensional): The simplest of the algorithms illustrated here. For an existing set there is only one position where a new cut can be placed – at the end of the last cut. Validation of a combination involves a simple Stock - Yield - Kerf = Scrap calculation.
- Plate (2-dimensional): These algorithms are significantly more complex. For an existing set, there may be as many as eight positions where a new cut may be introduced next to each existing cut, and if the new cut is not perfectly square then different rotations may need to be checked. Validation of a potential combination involves checking for intersections between two-dimensional objects.
- Packing (3-dimensional): These algorithms are the most complex illustrated here due to the larger number of possible combinations. Validation of a potential combination involves checking for intersections between three-dimensional objects.
