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Fastener engineer here!

Torque is a means to an end. The end goal is actually to stretch the screw. The screw behaves like a rubber band. As you stretch it out, it wants to return to its original length. As it does so, it squeezes the components you are fastening together. This is typically called clamp load. This clamp load is difficult to measure directly, requiring modifications to include an expensive load cell or ultrasonic measurement. Neither of these are practical in any production environment, and basically impossible at home. Torque on the other hand is really easy to cheaply measure.

What dictates how much clamp load you can get for a given torque is the friction you have to overcome as you tighten and how much clamp load the bolt can sustain. The total load be the proof load of the part and is based on the grade. Friction primarily comes from the contact between the threads and the contact between the underside of the screw's head and your joint components. This friction is also what prevents the screw from coming loose on its own. The amount of friction must be controlled so that you require a consistent amount of torque to reach a consistent amount of clamp load. This is typically done by careful control of the screws finish and application of lubricants.

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There is a pretty simple formula for figuring out approximate torque if you know a few things about the components.

T = KDP

T= torque

K= dimensionless friction value for the entire joint. Takes into consideration the finishes and geometry of all of the components. Can be approximated in non-critical joints, for safety critical should always be experimentally derived.

D= nominal thread diameter

P= clamp load, often 75% of the proof load of the bolt

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If for example I wanted to figure out what torque I should tighten a 1/4-20 grade 5 hex cap screw into a joint that has a matching nut or a tapped hole at least 3/8 deep:

K= .22 (typical for zinc plated parts, would change if using something with significantly different geometry like a flange bolt, different finish, or with lubricant added.)

D= .250 in

P= 2025 lbf (75% proof load)

.22 * .250 * 2025 = 111 in-lb

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One consideration for the DIY at home is that lubrication will often reduce the amount of friction, meaning LESS torque is required to reach the required clamp load. The consequence here is that the torque value your service manual says you should use may actually be enough to damage the threads if you use lubricants that were not originally used during manufacturing.

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Proportionally, how large are the antigens to the size of a blood cell?

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Get a torque wrench of the appropriate range (in/lbs, ft/lbs, etc), set it to the desired amount and tighten the screw/s. The wrench will let you know when you've reached the setting--it will click, release, illuminate or such. Specific tightening techniques can be required for certain applications such as engine head bolts, wheels, etc.--bolts may be required to be tightened in a certain order/pattern/amount, multiple passes, etc.

Exactly. And that was all done within the context of a tribe

It didn't need to be somehow shipped to a different plant just so that they can extract the hooves and re use those

Be nice if we synthesized these sorts of things, assuming it is equally as effective

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I totally understand the impetus with meat products to maximize gains and make everything a product for profit's sake, but as you also illustrated that Epinephrine being for medical use might restrict how it can be harvested.

That was kind of the nuts and bolts of what I was asking about, I've tried googling around because I'm curious and its not the easiest to find citable sources on whether meat or dairy cows are also harvested for their adrenal glands. I suppose the biggest question is how much more difficult it is to synthesize vs harvest, as I'm sure whichever is easier and cheaper is where most of it comes from.

Blood types can be visualized under a light microscope if IgM antibodies are added to the blood. This is a very fast and easy way to determine blood type. What you actually see is the blood cells clumping together (the actual A, B, and Rh proteins are far too small to be seen except perhaps by very specialized equipment).

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A shows a normal blood smear and B shows agglutination from antibody binding blood type A antigen. From article by Yang, X. et al. from 2011.

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It's fine to ask the question, but you've already reached a conclusion despite acknowledging that you don't know the specifics.

The power output does not scale linearly. The blades interfere with each other, so the more blades you put on a turbine, the less power each blade generates. Same reason nobody builds biplanes anymore - one long wing is more efficient than two shorter ones, one on top of another.

In fact, two blades would be more efficient (but less stable), and there are even examples of single blade turbines out there.

Sterile Neutrinos as dark matter has been ruled out, an experiment running next to a reactor has found no sign of them.

https://phys.org/news/2023-01-results-stereo-sterile-neutrino-hypothesis.amp

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I don't actually have first hand knowledge but slaughtering livestock just for adrenal glands simply makes no sense. A meat processing facility will buy livestock, slaughter them, process them and then sell the various components. There's a certain demand by companies making compounds such as epinephrine for animal parts used in synthesis. They will go through a supplier that will purchase animal parts from meat processor that they know they can sell at a certain price. I'm sure for at least some meat processors it's more lucrative to separate out kidneys with adrenal glands to sell separately at a higher price than e.g. just sell them together with all the rest to a company making dog food.

There are a number of ways that give you different results depending on what your goal is. The more complicated the AI, the more complicated the training process is, but the most common way is built on this procedure:

1. Give the AI an example of the task you want it to do. If you want the AI to learn how to add numbers, give it the two input numbers. If you want the AI to learn how to play chess, you give it a chess puzzle.
2. See what the AI gives you as the solution. For the two examples in (1), the AI would give you a number, and a chess move, respectively as the outputs.
3. Look at the internals of the AI and tweak the way the AI thinks until the answers given to those questions are right.
4. Repeat, with different examples, and eventually all of your tweaks will add up to an AI that can do the task.

Modern AI is built on Neural Networks, which were technically invented in 1943 as a computerized analogy to Neurons. However, they've been modified a bunch since then, and no longer resemble neurons in a lot of relevant ways. The real breakthrough in Neural Nets was when (1) it was discovered that GPUs, which were invented for graphics, could also be used for Neural Nets, and (2) a method of training "deep" neural networks, where there are many layers of neurons between the input and output, was invented. Before (2), neural networks were limited to 5-10 layers, because we couldn't figure out how to do step (3) in the above list on deeper neural nets. Modern neural networks can have hundreds of layers.

If you want to dive deeper into the mechanics of what a "neural network" actually is, you can watch https://www.youtube.com/watch?v=aircAruvnKk.

The other thing unlocking modern AI, beyond having the ability to train deep neural networks on GPUs, is lots of examples. The breakthrough for training AI to solve the board game Go, for example, was figuring out a way to train the AI via letting it play itself billions of times. This is hard because you can't know if a move is good or not until the end of the game.

One thing you should always be careful of when evaluating an AI is ask "what was it actually trained to do?" For example, consider ChatGPT. ChatGPT was not trained to "answer questions usefully", it was trained on the internet with the task of "given the first 1000 words of this website, guess the next word." It turns out if you take this "next word prediction machine" and repeatedly feed its best-guess output back into it as input, it can write paragraphs of comprehensible text. But it's not _trying_ to write comprehensive text, it's trying to predict the next word. This can make some of the weird ways it behaves make more sense. For example, once it's made a single mistake, it's more likely to make more mistakes, because it thinks (using this word as a analogy, who knows whether neural nets can really think) "huh, there's some mistakes in the previous text, I will guess there will be more mistakes in the rest of the test".

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Would the later emitted photon necessarily have a lower frequency? If the electron is moving between the same two shells it would be the same frequency, right?

I would assume that its use as a durg would mean you can't just extract from meat animals, similar to the chicken farms whose sole purpose is to provide clean eggs for vaccine production you'd probably have a farm growing cows or whatever specifically to get the epinephrine (if you didn't synthesize it).

That being said, extracting more profit is the name of the game, slaughterhouses absolutely find ways to use all of the animal if it can be done. Why raise a whole cow only to sell the steaks? The offal, blood, bones, off cuts and little bits of remaining meat all have uses. That's why pink slime exists, trying to extract all the meat, even if you've got to sanitize it and press it into nuggets.

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