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: Sounds like you are talking about “Distributed memory systems” which use multiple computers to solve a common problem, with computation distributed among the connected computers (nodes) and using message-passing to communicate between the nodes.
Vehicle maintenance is important because without them the vehicle would not function properly and could be a safety hazard. Vehicle safety is important because without it we could end up seriously injured or even killed, vehicles can be very dangerous if handled irresponsible and the proper safety methods are not used.<span />
Answer:
Explanation:
Transitive dependency
In this case, we have three fields, where field 2 depends on field 1, and field three depends on field 2.
For example:
Date of birth --> age --> vote
Partial dependency
It is a partial functional dependency if the removal of any attribute Y from X, and the dependency always is valid
For example:
Course and student these tables have a partial dependency, but if we have the field registration date, this date will depend on the course and student completely, we must create another table with the field registration date to remove this complete dependency.
If we remove or update the table registration date, neither course nor student must not change.
We need more information for this one, please.
