Answer:
//The program prompts user to input three integers and it displays them, adds and gets their average
//begin
public class Test
{
public static void Main()
{
//input intergers
int[] score = new int[3];
int avg,rem,sum = 0;
for(int i=0;i<3;i++)
{
Console.WriteLine("Enter an integer score ");
score[i] = Convert.ToInt32(Console.ReadLine());
sum = sum + score[i];
}
avg = sum/3;
rem = sum%3;
Console.WriteLine("The average of "+score[0]+","+score[1]+","+score[2]+" is "+avg +" with a remainder of "+rem);
}
}
Features of Multidimensional scaling(MDS) from scratch is described below.
Explanation:
Multidimensional scaling (MDS) is a way to reduce the dimensionality of data to visualize it. We basically want to project our (likely highly dimensional) data into a lower dimensional space and preserve the distances between points.
If we have some highly complex data that we project into some lower N dimensions, we will assign each point from our data a coordinate in this lower dimensional space, and the idea is that these N dimensional coordinates are ordered based on their ability to capture variance in the data. Since we can only visualize things in 2D, this is why it is common to assess your MDS based on plotting the first and second dimension of the output.
If you look at the output of an MDS algorithm, which will be points in 2D or 3D space, the distances represent similarity. So very close points = very similar, and points farther away from one another = less similar.
Working of MDS
The input to the MDS algorithm is our proximity matrix. There are two kinds of classical MDS that we could use: Classical (metric) MDS is for data that has metric properties, like actual distances from a map or calculated from a vector
.Nonmetric MDS is for more ordinal data (such as human-provided similarity ratings) for which we can say a 1 is more similar than a 2, but there is no defined (metric) distance between the values of 1 and 2.
Uses
Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of n objects or individuals" into a configuration of n points mapped into an abstract Cartesian space.
Answer: I would love to learn this
Explanation:
Answer:
The answer is below
Explanation:
a) The work done is equal to the loss in kinetic energy (KE)
Change in kinetic energy (ΔKE) = Final kinetic energy - initial kinetic energy
Final KE = 
But the final velocity is 0 (at rest). Hence:
Final KE = 
ΔKE = 0 - K = -K
W = ΔKE = -K
Also, the work done (W) = charge (q) * distance (d) * electric field intensity (E)
W = qEd
but q = -e, hence:
W = -e * E * d
Using:
W = ΔKE
-e * E * d = -K
E= K / (e * d)
b) The electric field is in the direction of the electrons motion
Answer:
No
Explanation:
Heat engine is a device which operates between two thermal reservoir.One thermal reservoir supply the thermal energy to the heat engine and another thermal reservoir absorb heat rejected by heat engine.These thermal reservoir are infinite amount of heat capacity system.Temperature of thermal reservoir remain constant always.
So we can say that heat engine can not operate by using only one thermal reservoir.
