Answer:
finding Cepheid variable and measuring their periods.
Explanation:
This method is called finding Cepheid variable and measuring their periods.
Cepheid variable is actually a type of star that has a radial pulsation having a varying brightness and diameter. This change in brightness is very well defined having a period and amplitude.
A potent clear link between the luminosity and pulsation period of a Cepheid variable developed Cepheids as an important determinants of cosmic criteria for scaling galactic and extra galactic distances. Henrietta Swan Leavitt revealed this robust feature of conventional Cepheid in 1908 after observing thousands of variable stars in the Magellanic Clouds. This in fact turn, by making comparisons its established luminosity to its measured brightness, allows one to evaluate the distance to the star.
(a) If the cornea were simply thin lens then power will be 43 diopters.
(b) This is a concave lens
The cornea is the transparent front part of the eye that covers the iris, pupil, and anterior chamber. Despite injury or disease, the cornea can still repair itself quickly. However, there are situations where damage is too severe for the cornea to heal on its own – such as with a deep injury to the cornea. The following symptoms may indicate that the cornea has sustained a substantial infection, injury or disease: Blurred vision Pain Redness.
Along with the anterior chamber and lens, the cornea refracts light, accounting for approximately two-thirds of the eye's total optical power. In humans, the refractive power of the cornea is approximately 43 diopters.
There are two types of lenses: converging and diverging and here if the cornea was simply thin then the diverging or concave lens is used in the eyes which is thin in the center than their edges.
To know more about cornea, refer: brainly.com/question/13866057
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There are several ways they interact with each other.
Mechanically: Erosion, Mass movement, Sedimentation
Answer:
The independent variable is the number of dry cells and the dependent variable is the time the bulb works.
Explanation:
In this exercise, you are asked to analyze the variables derived from Ómar's hypotypeis
"If more dry cells are connected end-to-end, a light bulb will work longer because more energy is available."
In this hypothesis, the independent variable that is controlled by the researcher is the number of batteries to be connected in series.
The dependent variable that is measured by the researcher is how long the bulbs last.
When reviewing the different answers, the correct one is:
The independent variable is the number of dry cells and the dependent variable is the time the bulb works.
Answer:
(D) 4
Explanation:
The percentage error in each of the contributors to the calculation is 1%. The maximum error in the calculation is approximately the sum of the errors of each contributor, multiplied by the number of times it is a factor in the calculation.
density = mass/volume
density = mass/(π(radius^2)(length))
So, mass and length are each a factor once, and radius is a factor twice. Then the total percentage error is approximately 1% +1% +2×1% = 4%.
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If you look at the maximum and minimum density, you find they are ...
{0.0611718, 0.0662668} g/(mm²·cm)
The ratio of the maximum value to the mean of these values is about 1.03998. So, the maximum is 3.998% higher than the "nominal" density.
The error is about 4%.
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<em>Additional comment</em>
If you work through the details of the math, you will see that the above-described sum of error percentages is <em>just an approximation</em>. If you need a more exact error estimate, it is best to work with the ranges of the numbers involved, and/or their distributions.
Using numbers with uniformly distributed errors will give different results than with normally distributed errors. When such distributions are involved, you need to carefully define what you mean by a maximum error. (By definition, normal distributions extend to infinity in both directions.) While the central limit theorem tends to apply, the actual shape of the error distribution may not be precisely normal.
