Answer:
Technician A and Technician B both are correct.
Explanation:
Technician A accurately notes that perhaps the forms of combustion process most widely used are hemispherical and cross.
Technician B also correctly notes that in several cylinder heads, cooling system and greases gaps and pathways are found.
Answer:
please mark me as a brainleast
Explanation:
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Answer:

Explanation:
First we calculate the mass of the aire inside the rigid tank in the initial and end moments.
(i could be 1 for initial and 2 for the end)
State1


State2


So, the total mass of the aire entered is

At this point we need to obtain the properties through the tables, so
For Specific Internal energy,

For Specific enthalpy

For the second state the Specific internal Energy (6bar, 350K)

At the end we make a Energy balance, so

No work done there is here, so clearing the equation for Q



The sign indicates that the tank transferred heat<em> to</em> the surroundings.
Answer:
Made of Silicon.
Explanation:
A diode is a semiconductor device use in mostly electronic appliances. It is two terminals device consisting of a P-N junction formed either in Germanium or silicon crystal.
Diode can be forward biased or reverse biased.
When a diode is forward biased and the applied voltage is increased from zero, hardly any current flows through the device in the beginning.
It is so because the external voltage is being opposed by the internal barrier voltage whose value is 0.7v for silicon and 0.3v for germanium.
If you measure 0.7 V across a diode, the diode is probably therefore made of Silicon.
Answer:
Explanation:
The python code to generate this is quite simple to run.
i hope you understand everything written here, you can as well try out other problems to understand better.
First to begin, we import the package;
Code:
import pandas as pd
import matplotlib.pyplot as plt
name = input('Enter name of the file: ')
op = input('Enter name of output file: ')
df = pd.read_csv(name)
df['Date'] = pd.to_datetime(df["Date"].apply(str))
plt.plot(df['Date'],df['Absent']/(df['Present']+df['Absent']+df['Released']),label="% Absent")
plt.legend(loc="upper right")
plt.xticks(rotation=20)
plt.savefig(op)
plt.show()
This should generate the data(plot) as seen in the uploaded screenshot.
thanks i hope this helps!!!