Answer:
Explanation:
The following code is written in Python. It continues looping and asking the user for an oligonucleotide sequence and as long as it is valid it outputs the reverse complement of the sequence. Otherwise it exits the loop
letters = {'A', 'C', 'G', 'T'}
reloop = True
while reloop:
sequence = input("Enter oligonucleotide sequence: ")
for x in sequence:
if x not in letters:
reloop = False;
break
if reloop == False:
break
newSequence = ""
for x in sequence:
if x == 'A':
newSequence += 'T'
elif x == 'T':
newSequence += 'A'
elif x == 'C':
newSequence += 'G'
elif x == 'G':
newSequence += 'C'
print("Reverse Complement: " + newSequence)
Answer:
O(N!), O(2N), O(N2), O(N), O(logN)
Explanation:
N! grows faster than any exponential functions, leave alone polynomials and logarithm. so O( N! ) would be slowest.
2^N would be bigger than N². Any exponential functions are slower than polynomial. So O( 2^N ) is next slowest.
Rest of them should be easier.
N² is slower than N and N is slower than logN as you can check in a graphing calculator.
NOTE: It is just nitpick but big-Oh is not necessary about speed / running time ( many programmers treat it like that anyway ) but rather how the time taken for an algorithm increase as the size of the input increases. Subtle difference.
This belief reflects an omnipotent view of management.
Below, I believe are the multiple choices attached to this question
A. Power, Structural, and Technical Systems.
B. Construction.
C. Design/ Pre-Construction.
D. Maintenance/ Operations
The answer is A: Power, Structural, and Technical Systems.
The career pathways in the Architecture and Construction deal with all aspects of designing, planning, and maintaining all kinds of structures we live or work in. It is organized into 3 career pathways; Construction, Design/ PreConstruction, and Maintenance/ Operations. This field also covers the servicing of equipment such as plumbing, electrical wiring, escalators, and elevators.
