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Acetylene and propane
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Disability is extremely diverse. While some health conditions associated with disability result in poor health and extensive health care needs, others do not. However, all people with disability have the same general health care needs as everyone else, and therefore need access to mainstream health care services.
The component of a regenerative vapor power cycle that permits only liquid to pass through to a region of lower pressure is a Valve/trap.
<h3>What is vapor?</h3>
- In physics, a substance in the gas phase at a temperature lower than its critical temperature is referred to as a vapor or vapor.
- This means that the vapor can be condensed to a liquid by increasing the pressure on it without decreasing the temperature.
- An aerosol is distinct from a vapor.
- A suspension of minute liquid, solid, or both types of particles in a gas is known as an aerosol.
<h3>Why does vapor form?</h3>
- Evaporation or sublimation are two processes that can be used to create it.
- Unlike clouds, fog, or mist, which are only suspended drops of liquid water in the atmosphere, watevaporur is a gas and cannot be seen.
- In the atmosphere, water vapor frequently exists below the boiling point.
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Answer:Decay rate constant,k = 0.00376/hr
Explanation:
IsT Order Rate of reaction is given as
In At/ Ao = -Kt
where [A]t is the final concentration at time t and [A]o is the inital concentration at time 0, and k is the first-order rate constant.
Initial concentration = 80 mg/L
Final concentration = 50 mg/L
Velocity = 40 m/hr
Distance= 5000 m
Time taken = Distance / Time
5000m / 40m/hr = 125 hr
In At/ Ao = -Kt
In 50/80 = -Kt
-0.47 = -kt
- K= -0.47 / 125
k = 0.00376
Decay rate constant,k = 0.00376/hr
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.