Sorry to answer you so late but that would be truth because that is the first rule of science which is what thermodynamics depends in.
The first law of thermodynamics is a type of law of conservation but with thermodynamics systems.
So, in gogle I found this: "The law of conservation of energy states that the total energy of an isolated system is constant; energy can be transformed from one form to another, but cannot be created or destroyed."
Like I said, sorry to reply so late but I hope this helped! =)
Give the type and value of each result of the following Java expressions. a. (5 / 2) * 2.0 type is float value is 4.0 b. (5/2.0) * 2 type is integer value is 5c. "1.3" + "5.2" type is string value is 1.35.2d. 1 + 7.0 + "2" + "x" this will produce an error because we cannot add variables of different type
Answer:
You need to get everything in version control. You need to automate the entire environment creation process.
Explanation:
You need a deployment pipeline where you can create test and production environments, and then deploy code into them, entirely on demand.
hope this helped ^^
Answer:
B . It amplifies the power to speed up the motor and generate more torque
Big-O notation is a way to describe a function that represents the n amount of times a program/function needs to be executed.
(I'm assuming that := is a typo and you mean just =, by the way)
In your case, you have two loops, nested within each other, and both loop to n (inclusive, meaning, that you loop for when i or j is equal to n), and both loops iterate by 1 each loop.
This means that both loops will therefore execute an n amount of times. Now, if the loops were NOT nested, our big-O would be O(2n), because 2 loops would run an n amount of times.
HOWEVER, since the j-loop is nested within i-loop, the j-loop executes every time the i-loop <span>ITERATES.
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As previously mentioned, for every i-loop, there would be an n amount of executions. So if the i-loop is called an n amount of times by the j loop (which executes n times), the big-O notation would be O(n*n), or O(n^2).
(tl;dr) In basic, it is O(n^2) because the loops are nested, meaning that the i-loop would be called n times, and for each iteration, it would call the j-loop n times, resulting in n*n runs.
A way to verify this is to write and test program the above. I sometimes find it easier to wrap my head around concepts after testing them myself.
