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Leading and trailing whitespace characters in s are ignored. Whitespace is removed as if by the String.trim() method; that is, both ASCII space and control characters are removed. The rest of s should constitute a FloatValue as described by the lexical syntax rules: FloatValue: Signopt NaN Signopt Infinity Signopt FloatingPointLiteral Signopt HexFloatingPointLiteral SignedInteger HexFloatingPointLiteral: HexSignificand BinaryExponent FloatTypeSuffixopt HexSignificand: HexNumeral HexNumeral . 0x HexDigitsopt . HexDigits 0X HexDigitsopt . HexDigits BinaryExponent: BinaryExponentIndicator SignedInteger BinaryExponentIndicator: p P where Sign, FloatingPointLiteral, HexNumeral, HexDigits, SignedInteger and FloatTypeSuffix are as defined in the lexical structure sections of The Java Language Specification, except that underscores are not accepted between digits. If s does not have the form of a FloatValue, then a NumberFormatException is thrown. Otherwise, s is regarded as representing an exact decimal value in the usual "computerized scientific notation" or as an exact hexadecimal value; this exact numerical value is then conceptually converted to an "infinitely precise" binary value that is then rounded to type double by the usual round-to-nearest rule of IEEE 754 floating-point arithmetic, which includes preserving the sign of a zero value. Note that the round-to-nearest rule also implies overflow and underflow behaviour; if the exact value of s is large enough in magnitude (greater than or equal to (MAX_VALUE + ulp(MAX_VALUE)/2), rounding to double will result in an infinity and if the exact value of s is small enough in magnitude (less than or equal to MIN_VALUE/2), rounding to float will result in a zero. Finally, after rounding a Double object representing this double value is returned.

Note that trailing format specifiers, specifiers that determine the type of a floating-point literal (1.0f is a float value; 1.0d is a double value), do not influence the results of this method. In other words, the numerical value of the input string is converted directly to the target floating-point type. The two-step sequence of conversions, string to float followed by float to double, is not equivalent to converting a string directly to double. For example, the float literal 0.1f is equal to the double value 0.10000000149011612; the float literal 0.1f represents a different numerical value than the double literal 0.1. (The numerical value 0.1 cannot be exactly represented in a binary floating-point number.)

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In all cases, the result is a long integer that, when given to the longBitsToDouble(long) method, will produce a floating-point value the same as the argument to doubleToLongBits (except all NaN values are collapsed to a single "canonical" NaN value).

If the argument is NaN, the result is the long integer representing the actual NaN value. Unlike the doubleToLongBits method, doubleToRawLongBits does not collapse all the bit patterns encoding a NaN to a single "canonical" NaN value.

If the argument is any value in the range 0x7ff0000000000001L through 0x7fffffffffffffffL or in the range 0xfff0000000000001L through 0xffffffffffffffffL, the result is a NaN. No IEEE 754 floating-point operation provided by Java can distinguish between two NaN values of the same type with different bit patterns. Distinct values of NaN are only distinguishable by use of the Double.doubleToRawLongBits method.

Note that this method may not be able to return a double NaN with exactly same bit pattern as the long argument. IEEE 754 distinguishes between two kinds of NaNs, quiet NaNs and signaling NaNs. The differences between the two kinds of NaN are generally not visible in Java. Arithmetic operations on signaling NaNs turn them into quiet NaNs with a different, but often similar, bit pattern. However, on some processors merely copying a signaling NaN also performs that conversion. In particular, copying a signaling NaN to return it to the calling method may perform this conversion. So longBitsToDouble may not be able to return a double with a signaling NaN bit pattern. Consequently, for some long values, doubleToRawLongBits(longBitsToDouble(start)) may not equal start. Moreover, which particular bit patterns represent signaling NaNs is platform dependent; although all NaN bit patterns, quiet or signaling, must be in the NaN range identified above.

The Double value type represents a double-precision 64-bit number with values ranging from negative 1.79769313486232e308 to positive 1.79769313486232e308, as well as positive or negative zero, PositiveInfinity, NegativeInfinity, and not a number (NaN). It is intended to represent values that are extremely large (such as distances between planets or galaxies) or extremely small (such as the molecular mass of a substance in kilograms) and that often are imprecise (such as the distance from earth to another solar system). The Double type complies with the IEC 60559:1989 (IEEE 754) standard for binary floating-point arithmetic.

Compares this instance to a specified double-precision floating-point number and returns an integer that indicates whether the value of this instance is less than, equal to, or greater than the value of the specified double-precision floating-point number.

Converts the span representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Converts a character span containing the string representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Converts the string representation of a number in a specified style and culture-specific format to its double-precision floating-point number equivalent. A return value indicates whether the conversion succeeded or failed.

Double precision floating point number. On the Uno and other ATMEGA based boards, this occupies 4 bytes. That is, the double implementation is exactly the same as the float, with no gain in precision.

The generic term he uses is a Test Double (think stuntdouble). Test Double is a generic term for any case where youreplace a production object for testing purposes. There are variouskinds of double that Gerard lists:

In the IEEE 754-2008 standard, the 64-bit base-2 format is officially referred to as binary64; it was called double in IEEE 754-1985. IEEE 754 specifies additional floating-point formats, including 32-bit base-2 single precision and, more recently, base-10 representations (decimal floating point).

One of the first programming languages to provide floating-point data types was Fortran.[citation needed] Before the widespread adoption of IEEE 754-1985, the representation and properties of floating-point data types depended on the computer manufacturer and computer model, and upon decisions made by programming-language implementers. E.g., GW-BASIC's double-precision data type was the 64-bit MBF floating-point format.

Double-precision binary floating-point is a commonly used format on PCs, due to its wider range over single-precision floating point, in spite of its performance and bandwidth cost. It is commonly known simply as double. The IEEE 754 standard specifies a binary64 as having:

The double-precision binary floating-point exponent is encoded using an offset-binary representation, with the zero offset being 1023; also known as exponent bias in the IEEE 754 standard. Examples of such representations would be:

Using double-precision floating-point variables is usually slower than working with their single precision counterparts. One area of computing where this is a particular issue is parallel code running on GPUs. For example, when using NVIDIA's CUDA platform, calculations with double precision can take, depending on hardware, from 2 to 32 times as long to complete compared to those done using single precision.[4]

C and C++ offer a wide variety of arithmetic types. Double precision is not required by the standards (except by the optional annex F of C99, covering IEEE 754 arithmetic), but on most systems, the double type corresponds to double precision. However, on 32-bit x86 with extended precision by default, some compilers may not conform to the C standard or the arithmetic may suffer from double rounding.[5]

Double-duty actions include interventions, programmes and policies that have the potential to simultaneously reduce the risk or burden of both undernutrition (including wasting, stunting and micronutrient deficiency or insufficiency) and overweight, obesity or diet-related NCDs. Reflecting the shared drivers and platforms of contrasting forms of malnutrition, double duty can be achieved at three levels: through doing no harm with regard to existing actions on malnutrition; by retrofitting existing nutrition actions to address or improve new or other forms of malnutrition; and through the development of de-novo, integrated actions aimed at the double burden of malnutrition.

The coexistence of contrasting forms of malnutrition is known as the double burden of malnutrition. A global challenge, this double burden is united by shared drivers and solutions and therefore offers a unique opportunity for integrated nutrition action. This policy brief sets out the potential for double-duty actions to contribute to this intensified effort by addressing both sides of malnutrition through common interventions.

To pursue a double major in AEM, you must be a full-time Cornell CALS student in good standing and have demonstrated proficiency in math, economics, and statistics. Additionally, you should be able to articulate how the AEM major aligns with your current academic and career goals. 006ab0faaa