I know what it is but its hard to see
Answer:
A. Ein = 8.05*10^-4 V/m
B. Clockwise sense
Explanation:
A. the magnitude of the electric field induced in the ring is obtaind by using the following formula:
(1)
Ein: induced electric field
ds: differential of a path of the ring
ФB: magnetic flux in the ring
The Ein vector is parallel to ds in the complete ring. Furthermore, the area of the ring is constant, hence, you have in the equation (1):
(2)
dB/dt = -0.280T/s (it is decreasing)
A: area of the ring = π(r/2)^2= (π/4) r^2
r: radius of the ring = 4.60/2 = 2.30 cm
Then, you replace the values of all variables in the equation (2):

hence, the induced electric field is 8.05*10^-4 V/m
B. The induced current in the ring produced a magnetic field that is opposite to the magnetic field of the magnet. The, in this case you have that the induced current is in a clockwise sense.
Answer:A::C
Solution :
ΔT=
1
2
TαΔθ
rArr Δt=
ΔT
T1t
But T′≈T
∴Δt=
1
2
αΔθ(t)
=
1
2
×0.000012×20×24×3600
=10.37s
At higher temperture, length of pendulum clock will be more. So, time period will be more and it will lose the time.
Explanation:
fiber-optic cable is composed of two concentric layers, called the core and the cladding, as illustrated in Figure 3-1. The core and cladding have different refractive indices, with the core having a refractive index of n1, and the cladding having a refractive index of n2. The index of refraction is a way of measuring the speed of light in a material. Light travels fastest in a vacuum. The actual speed of light in a vacuum is 300,000 kilometers per second, or 186,000 miles per second.
Data’s conclusion is considered
valid if the experiment validity is being controlled. With this conclusion
validity, our data gathered and presented are considered a reasonable one.
It is the most important types of validity. As what is said, by having conclusion
validity, your data will be undoubtedly least considered since it is labelled
as statistical and well-studied. Once you already mention the word
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race and includes relationship in your data.