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2 years ago

Device management is the process of managing

1 answer:

8 0

Device management is the process of managing the devices in your computer. In your computer you have the so-called device manager, in which all the drivers of your devices are installed. Device Manager allows you to monitor and manage each of its devices. You can also uninstall and update your devices in device manager. And the process of doing it is called device management. It is very simple to do this kind of stuffs as long you are familiar with your devices like mouse, printer, keyboard, etc.

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You just read a restaurant review on the internet of the newest restaurant in town. The writer of the article said the food was

Marizza181 [45]

A. The restaurant review contains data but is not data itself.
I'm sorry if that's wrong, but that seems right to me.

6 0

3 years ago

Create a program that allows the user to input a list of first names into one array and last names into a parallel array. Input

natita [175]

Answer:

The program in Python is as follows:

firstName = []

lastName = []

while True:

fName = input("First Name: ")

lName = input("Last Name: (Enter to quit): ")

if not lName:

break

firstName.append(fName)

lastName.append(lName)

for i in range(len(firstName)):

print(firstName[i] . lower()+"."+lastName[i] . lower()+" mycollege . edu")

Explanation:

See attachment for complete source file where comments are used as explanation

Download txt

8 0

3 years ago

The function of the __________ is to on transmission assemble data into a frame, on reception disassemble frame and perform addr

iris [78.8K]

Answer: Media access control layer (MAC layer)

Explanation:

The media access control layer is also known as MAC layer. The main function of the media access control layer is that it is used in the transmission of the data into the frame and perform the error detection and the address recognition which basically govern the accessing to the local area network (LAN) transmission medium.

The media access control layer is basically responsible for transmission of the data packets from NIC (Network interface card) to the another channel.

8 0

3 years ago

What are the main advantages of the d-step tuning of 2023 murano’s xtronic cvt®?.

DedPeter [7]

The main advantages of the d-step tuning of 2023 Murano's xtronic cvt is that:

It gives room for the transmission to quickly change ratios in regards to quick acceleration.
When at higher rpm, it help one to stepped acceleration via the use of linear acceleration feel.

<h3>What kind of transmission does a Nissan Murano have?</h3>

It is known to have a continuously variable automatic transmission (CVT) and it is one whose only engine is said to give about a 3.5-liter V-6 that helps to bring about a lot of power.

Note that the main advantages of the d-step tuning of 2023 Murano's xtronic cvt is that:

It gives room for the transmission to quickly change ratios in regards to quick acceleration.
When at higher rpm, it help one to stepped acceleration via the use of linear acceleration feel.

Learn more about cars from

brainly.com/question/25749514

#SPJ1

6 0

2 years ago

In order to consolidate your theoretical knowledge into technique and skills with practical and applicational value, you will us

Arisa [49]

Answer:

Check the explanation

Explanation:

Lasso: R example

To run Lasso Regression you can re-use the glmnet() function, but with the alpha parameter set to 1.

# Perform 10-fold cross-validation to select lambda --------------------------- lambdas_to_try <- 10^seq(-3, 5, length.out = 100) # Setting alpha = 1 implements lasso regression lasso_cv <- cv.glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = TRUE, nfolds = 10) # Plot cross-validation results plot(lasso_cv)

Best cross-validated lambda lambda_cv <- lasso_cv$lambda.min # Fit final model, get its sum of squared residuals and multiple R-squared model_cv <- glmnet(X, y, alpha = 1, lambda = lambda_cv, standardize = TRUE) y_hat_cv <- predict(model_cv, X) ssr_cv <- t(y - y_hat_cv) %*% (y - y_hat_cv) rsq_lasso_cv <- cor(y, y_hat_cv)^2 # See how increasing lambda shrinks the coefficients -------------------------- # Each line shows coefficients for one variables, for different lambdas. # The higher the lambda, the more the coefficients are shrinked towards zero. res <- glmnet(X, y, alpha = 1, lambda = lambdas_to_try, standardize = FALSE) plot(res, xvar = "lambda") legend("bottomright", lwd = 1, col = 1:6, legend = colnames(X), cex = .7)

Kindly check the Image below.

5 0

2 years ago