Answer:
Burj Khalifa
Explanation:
The world's tallest artificial structure is the 829.8-metre-tall (2,722 ft) Burj Khalifa in Dubai (of the United Arab Emirates). The building gained the official title of "tallest building in the world" and the tallest self-supported structure at its opening on January 9, 2010.
a brainliest would be appriciated
Maybe because of shipping costs and if you need to buy materials from a far place such as different international travels?
Answer:
The compressive stress of aplying a force of 708 kN in a 81 mm diamter cylindrical component is 0.137 kN/mm^2 or 137465051 Pa (= 137.5 MPa)
Explanation:
The compressive stress in a cylindrical component can be calculated aby dividing the compressive force F to the cross sectional area A:
fc= F/A
If the stress is wanted in Pascals (Pa), F and A must be in Newtons and square meters respectively.
For acylindrical component the cross sectional area A is:
A=πR^
If the diameter of the component is 81 mm, the radius is the half:
R=81mm /2 = 40.5 mm
Then A result:
A= 3.14 * (40.5 mm)^2 = 5150.4 mm^2
In square meters:
A= 3.14 * (0.0405 m)^2 = 0.005150 m^2
Replacing 708 kN to the force:
fc= 708 kN / 5150.4 mm^2 = 0.137 kN/mm^2
Using the force in Newtons:
F= 70800 N
Finally the compressive stress in Pa is:
fc= 708000 / 0.005150 m^2 = 137465051 Pa = 137 MPa
Answer:
Two Python codes are explained for the problem. Modify as appropriate
Explanation:
<u>CODE 1:</u>
def string_contains(input_string): # called function
if(input_string.__contains__('z')): # Check input_string contains 'z'
print('has the letter z.') # print input_string contains 'z'
else:
print('not worthwhile.') # print if input_string not contains 'z'
input_string = input('Please enter the string: ') # ACeept string from user
string_contains(input_string) # calling function where we pass input_string as actual parameter
<u>CODE 2:</u>
def string_contains(input_string):
for x in input_string:
if x=='z':
return 'has the letter z'
return 'not worthwhile'
Answer:
I can share a link to answer since the code is to long to be contained here. the code is on github
https://github.com/arafatm/edu_coursera_machine_learning_1_foundations/blob/master/code/02.01.predicting.house.prices.ipynb