Answer:
a) Q = 251.758 kJ/mol
b) creep rate is 
Explanation:
we know Arrhenius expression is given as
where
Q is activation energy
C is pre- exponential constant
At 700 degree C creep rate is
% per hr
At 800 degree C creep rate is
% per hr
activation energy for creep is 
= 
![\frac{1\%}{5.5 \times 10^{-2}\%} = e^{[\frac{-Q}{R(800+273)}] -[\frac{-Q}{R(800+273)}]}](https://tex.z-dn.net/?f=%5Cfrac%7B1%5C%25%7D%7B5.5%20%5Ctimes%2010%5E%7B-2%7D%5C%25%7D%20%3D%20e%5E%7B%5B%5Cfrac%7B-Q%7D%7BR%28800%2B273%29%7D%5D%20-%5B%5Cfrac%7B-Q%7D%7BR%28800%2B273%29%7D%5D%7D)
![\frac{0.01}{5.5\times 10^{-4}} = ln [e^{\frac{Q}{8.314}[\frac{1}{1073} - \frac{1}{973}]}]](https://tex.z-dn.net/?f=%5Cfrac%7B0.01%7D%7B5.5%5Ctimes%2010%5E%7B-4%7D%7D%20%3D%20ln%20%5Be%5E%7B%5Cfrac%7BQ%7D%7B8.314%7D%5B%5Cfrac%7B1%7D%7B1073%7D%20-%20%5Cfrac%7B1%7D%7B973%7D%5D%7D%5D)
solving for Q we get
Q = 251.758 kJ/mol
b) creep rate at 500 degree C
we know
Answer:Circular
Explanation:
It’s the only thing not list under pneumatic tools♂️
Answer:because it faster
Explanation: and when building a device or vehicle that requires large
Answer: output value
Explanation: Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
Supervised learning infers a function from labeled training data consisting of a set of training examples.
In supervised learning, each example consists of a pair of an input object (a vector) and a desired output value (the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples. An optimal scenario will allow for the algorithm to correctly determine the class labels for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way
A wide range of supervised learning algorithms are available, each having its own strengths and weaknesses.
You should now that, there is no single learning algorithm that works better than the other on all supervised learning problems
