Answer:
identity theft
Explanation:
Identity theft is the act of illegally taking another person's identity in order to maliciously use his/her credit card info or any other credentials. This is equally illegal as any other kind of theft, as it is almost the same as robbing someone real-time. This can be prevented by double authentications and secure passwords.
Answer:
the portfolio's return will be Ep(r)= 9.2 %
Explanation:
if the stock lies on the security market line , then the expected return will be
Ep(r) = rf + β*( E(M)- rf)
where
Ep(r) = expected return of the portfolio
rf= risk free return
E(M) = expected return of the market
β = portfolio's beta
then
Ep(r) = rf + β*( E(M)- rf)
E(M) = (Ep(r) - rf ) / β + rf
replacing values
E(M) = (Ep(r) - rf ) / β + rf
E(M) = ( 17.2% - 3.2%) /1.4 + 3.2% = 13.2%
since the stock and the risk free asset belongs to the security market line , a combination of both will also lie in this line, then the previous equation of expected return also applies.
Thus for a portfolio of β=0.6
Ep(r) = rf + β*( E(M)- rf) = 3.2% + 0.6*(13.2%-3.2%) = 9.2 %
Ep(r)= 9.2 %
Answer: D. A & C
Explanation:
A long term liability is one that is due to be paid in a period longer than a year. The loan is due in less than a year so the only way to classify it as a long term liability is to make it a loan that will extend past a year. This can be done through refinancing which is to replace the current loan with another loan.
Karin's company therefore would need to demonstrate that the obligation can be refinanced on a long-term basis by them and they must also have the intention to do so as well.
Answer:
I d speak this language sorry <3
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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