Answer:
import numpy as np#importing numpy module with an alias np.
def c(bitstring_array):# defining function c.
num_integer=bitstring_array.dot(2**np.arange(bitstring_array.size)[::-1])#bitstring conversion.
return num_integer#returning integer array.
print("Enter bits")
Bit_l=input().split(" ")#enter space separated bitstring.
for i in range(len(Bit_l)):#iterating over the bitstring.
Bit_l[i]=int(Bit_l[i])
bitstring_array=np.array(Bit_l)
print(c(bitstring_array))#function call.
Output:
Enter bits
1 1 1 0 0 1
57
Answer:
Check the explanation
Explanation:
An integer (int) is of two different bytes and each page has 200 bytes in length. What this means is that each row of array A (100 int) will fits perfectly in a page.
(a) For the initial or first array-initialization loop, one column is processed at a time, so a page fault will be generated at every inner loop iteration, with a total of 100*100=10,000 page faults.
(b) And when it comes to the second array-initialization loop, one row is processed at a time, and a page fault is generated at every outer loop iteration, with a total of 100 page faults.
Hence second array-initialization loop, has better spatial locality.
Answer:
The three quantitative characteristic properties of water is explained below in detail.
Explanation:
The three quantitative components of water incorporate the following:
1.Freezing point:
The water has a freezing point of 0 degrees Celsius.
2. Boiling point:
The water has a boiling point of 100 degrees Celsius.
3. Melting point:
The melting point of ice is 0 degrees Celsius.
These properties are all uncommon to water. Being uncommon means that these characteristics are only noticeable in water; hence, they can be beneficial in recognizing such a substance.
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