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The naming procedure for large numbers is based on taking the number n occurring in 10 3n+3 (short scale) or 10 6n (long scale) and concatenating Latin roots for its units, tens, and hundreds place, together with the suffix -illion. In this way, numbers up to 10 3·999+3 = 10 3000 (short scale) or 10 6·999 = 10 5994 (long scale
For example 1,000,000,000,000 rather than 1 trillion (short scale) or 1 billion (long scale). This method becomes unwieldy for very large numbers. Combinations of the unambiguous words such as ten, hundred, thousand and million. For example: one thousand million and one million million. [5]
The table shows what number the order of magnitude aim at for base 10 and for base 1 000 000. It can be seen that the order of magnitude is included in the number name in this example, because bi- means 2, tri- means 3, etc. (these make sense in the long scale only), and the suffix -illion tells that the base is 1 000 000 .
1/52! chance of a specific shuffle Mathematics: The chances of shuffling a standard 52-card deck in any specific order is around 1.24 × 10 −68 (or exactly 1 ⁄ 52!) [4] Computing: The number 1.4 × 10 −45 is approximately equal to the smallest positive non-zero value that can be represented by a single-precision IEEE floating-point value.
Thus the "order of magnitude" of a number (on a larger scale than usually meant), can be characterized by the number of times (n) one has to take the to get a number between 1 and 10. Thus, the number is between 10 ↑ ↑ n {\displaystyle 10\uparrow \uparrow n} and 10 ↑ ↑ ( n + 1 ) {\displaystyle 10\uparrow \uparrow (n+1)} .
The Natural Area Code, this is the smallest base such that all of 1 / 2 to 1 / 6 terminate, a number n is a regular number if and only if 1 / n terminates in base 30. 32: Duotrigesimal: Found in the Ngiti language. 33: Use of letters (except I, O, Q) with digits in vehicle registration plates of Hong Kong. 34
-yllion (pronounced / aɪ lj ən /) [1] is a proposal from Donald Knuth for the terminology and symbols of an alternate decimal superbase [clarification needed] system. In it, he adapts the familiar English terms for large numbers to provide a systematic set of names for much larger numbers.
1.1 × 10 25 bits – entropy increase of 1 mole (18.02 g) of water, on vaporizing at 100 °C at standard pressure; equivalent to an average of 18.90 bits per molecule. [24] 1.5 × 10 25 bits – information content of 1 mole (20.18 g) of neon gas at 25 °C and 1 atm; equivalent to an average of 25.39 bits per atom. [25] 2 86: 10 26: 2 89: 10 ...