The commonly used divide between pure iron and steel is around 0.02% carbon, between steel and cast iron is 2.14% of carbon. For more complex alloys, where there isn't just steel and carbon but a sizeable portion of other things, it can be complicated.

Pressure is defined as force per m^(2). So you can just get a piston of known size (say, 1 cm^(2)), check the force pushing on it, divide the force by size and then convert as you wish.

Also, strictly speaking kg/cm^(2) isn't a physically correct unit of pressure, it's implied that weight in normal gravity is used. "1 kg of force" is actually 1 kg * g (free fall acceleration on Earth, ~9.8 m/s^(2)), so 9.8 N of force.

Cast iron is even less "iron" than steel, it's just called that way. Iron is a base metal. Steel is an iron-carbon alloy with the right proportion of carbon, where the carbon makes it harder. "Cast iron" is not iron but it's another iron-carbon alloy with too much carbon, so the carbon makes it brittle.

This gets asked every week. Flat doesn't mean 2D in that context. Flat means "normal space" where parallel lines stay parallel, sum of angles in a triangle is 180 degrees and so on. For example, surface of a sphere is not flat: straight lines that are parallel in one place (for example, meridians at the equator) will converge in the other. This can be generalized to 3D space.

So, imagine playing a game: you are told a number, you add some X to that number and tell the result. You will be told if the result differs from the one expected by the person who told you the number, so you have to guess the correct X.

"1. What do we want as a result?"

"Well, maybe X = 0? 1+0=1, my answer is 1."

"No, for 1 we need something bigger. Let's try again, what do we want to get for 2?"

"Then maybe X = 2? 2+2=4, my answer is 4."

"No, we need less than that. Another try: what do we want for 3?"

"So, X is bigger than 0 but smaller than 2... Maybe X = 1? 3+1=4, my answer is 4."

"Yes, that's what we needed, you guessed the correct X!"

In this scenario, "take a number and add X to it" is your algorithm and X is a parameter for that algorithm. You don't know that parameter beforehand, you guess it in an iterative way only from the required answer.

Turns out, we can construct an algorithm with quite a lot of parameters (possibly, millions) in such a way that there will be possible values for that parameters which, in theory, will give us good results for the task at hand. Not perfect, but good. We don't know what exactly these values are, we only know that they can exist. The task can even be as complex as showing the algorithm an image of a bird and expecting the answer "bird", it still may work with some parameters unknown to us.

Learning methods allow the program, in a similar way to the example above, start with a completely random guess and then tweak all these parameters in a more or less sensible way only based on what the expected answer is. And the math goes in such a way that it will likely slowly find better and better combinations until it encounters something that actually works to an extent. This process is what's called machine learning and the set of values found for the parameters is called a model for this specific algorithm.

The child is pushing the ground back with his feet. So, forces acting on the child are balanced (so the child doesn't move back) but the wagon only gets one significant horizontal force (minus friction in the wheels) and doesn't accelerate.