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Marrrta [24]
3 years ago
13

Guess the output of this code: print( (3**2)//2)​

Engineering
1 answer:
Lorico [155]3 years ago
5 0

the answer is 2

kids simple just multiply 3 by 2 which gets the answer as 6 then divide 6 by 2 then it comes to again 2 and that is a simple answer...

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An alloy is evaluated for potential creep deformation in a short-term laboratory experiment. The creep rate is found to be 1% pe
LenaWriter [7]

Answer:

a) Q = 251.758 kJ/mol

b) creep rate is    = 1.751 \times 10^{-5} \% per hr

Explanation:

we know Arrhenius expression is given as

\dot \epsilon =Ce^{\frac{-Q}{RT}

where

Q is activation energy

C is pre- exponential constant

At 700 degree C creep rate is\dot \epsilon = 5.5\times 10^{-2}% per hr

At 800 degree C  creep rate is\dot \epsilon = 1% per hr

activation energy for creep is \frac{\epsilon_{800}}{\epsilon_{700}} = = \frac{C\times e^{\frac{-Q}{R(800+273)}}}{C\times e^{\frac{-Q}{R(700+273)}}}

\frac{1\%}{5.5 \times 10^{-2}\%} = e^{[\frac{-Q}{R(800+273)}] -[\frac{-Q}{R(800+273)}]}

\frac{0.01}{5.5\times 10^{-4}} = ln [e^{\frac{Q}{8.314}[\frac{1}{1073} - \frac{1}{973}]}]

solving for Q we get

Q = 251.758 kJ/mol

b) creep rate at 500 degree C

we know

C = \epsilon e^{\frac{Q}{RT}}

    =- 1\% e{\frac{251758}{8.314(500+273}} = 1.804 \times 10^{12} \% per hr

\epsilon_{500} = C e^{\frac{Q}{RT}}

                         = 1.804 \times 10^{12}  e{\frac{251758}{8.314(500+273}}

                         = 1.751 \times 10^{-5} \% per hr

4 0
3 years ago
1. One of these is NOT a type of pneumatic tool. Which one?
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Answer:Circular

Explanation:

It’s the only thing not list under pneumatic tools‍♂️

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Answer:because it faster

Explanation: and when building a device or vehicle that requires large

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Knowing that the central portion of the link BD has a uniform cross-sectional area of 800 2, determine the magnitude of the load
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Explanation:

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The term supervised learning refers to the fact that we give the learning algorithm a data set, in which the "right answers" wer
kenny6666 [7]

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.

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