Teapot spout placed on the bottom where the pressure is highest.
The spout shape change from large opening to smaller one. it is curved like an S(and not U) shape(like a river band?) to "straighten" the water getting rid of eddys which can form after the first curve(which you have to have to pour).
The spout has a sharp tip which prevent the pot from dripping (ig nobel prize winner, teapot effect)
The tea is poured from a high distant to the cup(laminar) which cools the tea and causes the water to splash which traps sand(remember we are in the Sahara) by making a foam along with the green tea saponin molecules which have of it attracted to water and the other repelled.
The sugar makes the liquid viscous along the mints' essential oils which keeps the bubbles.
Another fun use for gum arabic is making watercolor paints, you can do it with your kids sourcing the pigment from different soils. pour water and let the heavy big particle fall to the bottom and source the small ones from the top and mix some ingredients
Not sure what you are asking. The stone age way of making paint is to find some place where the ground has a weird colour, dig it up, clean it and you have a pigment.
Even to this day many of our paint pigments are mined this way. Red/yellow ochre, umber, sienna.
If what you are asking is the dirt in question geologically speaking a soil? Sometimes, sometimes not. It can be a sediment or a regolith too. But in the more general laymen sense callig any dirt from the ground a soil is not too mistaken.
Okay, but user070223 is talking about watercolour paints as an alternative use of gum arabic.
Now i’m just recognising this might be a language issue: watercolour paints are a type of paint to paint with on paper using a brush dipped in water. If you ever seen kids paint with brushes and paper most likely they were using watercolour paint.
Does any JIT/AOT/hot code optimization/techniques/compilers/runtime takes into account whether the branch prediction is saturated and try to recompile to go branchless
In general branchless is better for branches that can't be predicted 99.something %, saturating the branch prediction like this benchmark isn't a concern. The big concern is mispredicting a branch, then executing 300 instructions and having to throw them away once the branch is actually executed.
Her logic seems reasonable but stating that the fibers "return to their original crinkled state" is missing the fact that the fiber go through the process of spinning to improve tensile strength (as well as the options of making an infinite yarn from finite fibers by twisting them together). regardless to return to original "crinckled state" they need to overcome those forces as well as the forces of the geometry of the knit(on a different scale).
BTW Rayon is also made from cellulose, cellulose II. While Cellulose I(natural) is metastable it can be converted by disolving in lye to a stable form (beta-gllocouse molecolue chain goes from being parallel to being anti parllel which increases the # of hydrogen bonds as well as helping create a more stable 3d structure) which again improve tensile strength and resist wrinkles on a different scale.
Here's another youtuber journey to fix the lactose intolerance by just eating lactose(powdered milk)("prebiotic") which had strains of a bacteria("probiotic" which feasts on the prebiotic) that breaks down lactose survives in the microbiome
https://www.youtube.com/watch?v=h90rEkbx95w
Inspired by this post & TF comment I tried symbollic regression [0]
Basically it uses genetic algorithm to find a formula that matches known input and output vectors with minimal loss
I tried to force it to use pi constant but was unable
I don't have much expreience with this library but I'm sure with more tweaks you'll get the right result
from pysr import PySRRegressor
def f(n):
if n % 15 == 0:
return 3
elif n%5 == 0:
return 2
elif n%3 == 0:
return 1
return 0
n = 500
X = np.array(range(1,n)).reshape(-1,1)
Y = np.array([f(n) for n in range(1,n)]).reshape(-1,1)
model = PySRRegressor(
maxsize=25,
niterations=200, # < Increase me for better results
binary_operators=["+", "*"],
unary_operators=["cos", "sin", "exp"],
elementwise_loss="loss(prediction, target) = (prediction - target)^2",
with compleixty 22 loss: 0.000015800686
The first term is close to 2/3 * cos(2pi*n/3) which is featured in the actual formula in the article. the constant doesn't compare to 11/15 though
I didn't knew community/public whitelists exist, nor any browser extension that uses whitelists and blocks all other connections by default, like uMatrix does. Do you have any examples?
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