Answer:
<em>Translate the parent function, 2 units upward</em>
Step-by-step explanation:
Given

See attachment for the graph
Required
Determine the change in f(x) that gives the dashed line
Let the dash line be represented with g(x)
From the attachment, there is only one transformation from f(x) to the g(x).
When f(x) is translated 2 units vertically upwards
, it gives g(x); the dash line.
If
Then g(x) is:

Answer:
The cost of producing one bottle is $10
.
Explanation:
The fixed costs of making the drug = $1 million
The selling price of the 50 pills bottles = $10
Total number of bottles sold at breakeven =200000
Total revenue from the sale of bottle = $10 × 200000
Total revenue from the sale of bottle = $2000000
Since at breakeven point the total revenue is equal to total cost. So, total cost of producing the 200000 bottles is $2000000.
Thus, the cost of producing one bottle = $2000000 / 200000 = $10
Answer:
10.16%
Explanation:
The computation of the effective return for this investment is shown below:
Let us assume that we invested an amount in Australian dollars 100
The return is 8%
After one year, the amount is 108
Now the converting amount is 110.16 (108 × 102%)
Now the effective rate for this investment is
= 110.16 - 100
= 10.16%
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part.
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product.
Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Powder Bed Fusion Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues.
Learn more about Powder Bed Fusion here
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