Answer:
The Kinetic energy of the Cheeto truck = 130,500 J
Explanation:
The Formula for Kinetic energy is
K.E = 1/2 x M x V^2
So in this Situation Mass(M) is 290kg
Velocity (V) is 30 m/s
Inputting these values into our equation gives us
K.E = 1/2 x 290 x 30^2
30 times 30 = 900
So K.E = 1/2 x 290 x 900
Simplifing this we get : K.E = 290 x 450
So K.E = 130,500 Kgm^2/s^2
Now the unit's Kgm^2/s^2 scientific name is Joule and symbol (J).
So K.E = 130,500 J
P.S: Oh man, now I have a craving for some Cheetos!
Answer:
The magnetic flux through the coil is greatest when the plane of its area is perpendicular to the magnetic field
Explanation: from the formula magnetic flux @= BScos∆
At perpendicular ∆ will be zero making cos∆ =1
Answer:
Explanation:
Given data
Diameter D=0.50 mm
Wavelength λ=500 nm
Length L=2.0 m
To find
Width w
Solution
Circular aperture of diameter D will have bright central maximum of diameter:
w=(2.44λL)/D
Answer:
Diffusing the gradient ensures that most of the molecules in high concentration zone will wind up in the previously low concentration by the spontaneous movement of small molecules.
Explanation:
A gradient of concentration is the difference between in concentration of one place / area substance to different area. Having a molecule flow down its concentration gradient means moving the molecules from hypotonic areas to the concentration hypertonic areas
Diffusing the gradient ensures that most of the molecules in high concentration zone will wind up in the previously low concentration by the spontaneous movement of small molecules.
Answer:
c. Performs better on training data as the training process proceeds, while performing worse on a held-out test data
Explanation:
An over-fitted model is one that will perform best on training but would fail or do worse on a held-out test data.
Such models are optimum for a just a particular set of data but would grossly failed when extrapolated to some other data set not novel to it.
- Over-fitting a model implies that a model closely corresponds to a set of data but would not perform well with others.
- It is usually as a result of a model adapting the noise and other details of a particular data set and thereby incorporates it.
- This makes it difficult for the model to fit into another data set.