Answer:
C). rearview mirror (at least of four inches by four inches).
Explanation:
<u>A 'rearview mirror' would be needed in case one is required to pull an individual on water skis after a PWC(personal watercraft) that is rated for carrying two persons only</u>. This wide-angle mirror for rearview would allow the operator to monitor the person who is being towed constantly along with riding the ski at the same time. It is considered illegal to tow a person on a vessel without having a rearview mirror and at the same time, the limited capacity must also be followed strictly. Hence, <u>option C</u> is the correct answer.
Answer:
sectores industriales, comerciales o públicos, o para el uso doméstico.
Explanation:
Answer:
The rate of energy absorbed per unit time is 3500W.
Explanation:
From the question, we were given the following parameters;
Plane, opaque, gray, diffuse surface
â = 0.7
Surface area, A = 0.5m²
Incoming radiant energy, G = 10000w/m²
T = 500°C
Rate of energy absorbed is âAG;
âAG = 0.7 × 0.5 × 10000
âAG = 3500W.
The energy absorbed is measured in watts and denoted by the symbol W.
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.
