To solve this problem, we need to first convert the hexadecimal values into their IEEE-754 single precision binary format (32-bit format). The IEEE-754 format breaks the number into three parts: sign bit, exponent bits (8 bits), and fraction (mantissa) bits (23 bits).
Let's decode each number:
- \( R_A = 0xC1400000 \):
This corresponds to -8.0. In binary:
\[
0xC1400000 = 1100 \, 0001 \, 0100 \, 0000 \, 0000 \, 0000 \, 0000 \, 0000
\]
It represents -8.0 in floating point.
- \( R_B = 0x42100000 \):
This corresponds to 10.0. In binary:
\[
0x42100000 = 0100 \, 0010 \, 0001 \, 0000 \, 0000 \, 0000 \, 0000 \, 0000
\]
It represents 10.0 in floating point.
- \( R_C = 0x41400000 \):
This corresponds to 8.0. In binary:
\[
0x41400000 = 0100 \, 0001 \, 0100 \, 0000 \, 0000 \, 0000 \, 0000 \, 0000
\]
It represents 8.0 in floating point.
Now let's check the options:
- Option (A) \( A + C = 0 \):
Since \( A = -8.0 \) and \( C = 8.0 \), it follows that:
\[
A + C = -8.0 + 8.0 = 0
\]
This is true.
- Option (B) \( C = A + B \):
Since \( A = -8.0 \) and \( B = 10.0 \), we check:
\[
A + B = -8.0 + 10.0 = 2.0
\]
However, \( C = 8.0 \), so this is false. The correct relation would be \( C = A + B \) if the result was 8.0, but it is 2.0.
- Option (C) \( B = 3C \):
Since \( B = 10.0 \) and \( C = 8.0 \), we check:
\[
3C = 3 \times 8.0 = 24.0
\]
But \( B = 10.0 \), so this is false. This is incorrect because \( B \neq 3C \).
- Option (D) \( (B - C) > 0 \):
Since \( B = 10.0 \) and \( C = 8.0 \), we check:
\[
B - C = 10.0 - 8.0 = 2.0
\]
Since \( 2.0 > 0 \), this is true.
Therefore, the correct answer is (B).