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skelet666 [1.2K]
3 years ago
6

How can feeding plant crops to animals be considered an efficient use of those crops?

Engineering
1 answer:
stira [4]3 years ago
7 0

Answer:

Most of Our Grain is Being Consumed by Animals, Not Humans. How the Planting of Crops Used to Feed Livestock is Contributing to Habitat Destruction.

Explanation:

The major feed grains are corn, sorghum, barley, and oats.

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: Câu nào dưới đây thể hiện sự thiếu tự chủ?
sukhopar [10]

Answer:

thành thật mà nói bởi vì cách những chiếc lá đang chuyển và cách mặt trời chiếu sáng.

3 0
2 years ago
Diffusion of Ammonia in an Aqueous Solution Ammonia (A)-water (B) solution ta 278 K and 4 mm thick is in contact with an organic
Tom [10]

Answer:

Explanation:

The pictures below shows the whole explanation for the problem

4 0
3 years ago
If angle A is a complement of angle B, angle B and angle C are vertical angles, and the supplement of C has a measure of 140°. F
Over [174]

Answer:

50°

Explanation:

Complementary angles add up to 90°.

Supplementary angles add up to 180°.

Vertical angles are equal.

A + B = 90°

B = C

C = 180° − 140°

C = 40°

B = 40°

A = 50°

7 0
3 years ago
In this problem set, you will implement multidimensional scaling (MDS) from scratch. You may use standard matrix/vector librarie
EleoNora [17]

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.

8 0
3 years ago
In python/Spyder
lianna [129]

Answer:

Answer to both the parts A and B are provided in the word document attached.

Explanation:

The explanation of the programs is provided in the attached file along with the screenshot of ATM application output.

Download docx
8 0
3 years ago
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