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
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
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
A question for strcat / other Graphene developers:
Can you clarify what can one expect from legacy extended support. Will old devices get any more updates? how long, how often, is it just security patches etc..
I see you're quite active regarding FF. Nevertheless I'm not sure what you're talking about. I've installed Betterfox ESR on Windows, Linux and Android with no apparent issues.
In general at high enough depth the ocean temperature is constant[0].
At high latitude like the southern ocean it's constant at whatever depth.
I think the surface ambient temperature is below(cooler) the temperature where the water density is the highest around 4 degrees for pure water(water has negative thermal expansion which causes it to expand and float!), for southern ocean salinity is between 33-34 and maximum density is below 0[1] but still ambient might be lower which means the colder water is lighter.
I've skimmed the result couple of weeks ago, and if I remember it states that there from all machines which takes t time to accomplish something there is one such that sqrt(t) memory is used. so it's at most but not on avg nor amorotized[not sure if et even make sense it the space where the problem lies] (you take the least memory hungry machine)
Inspired by Steve Brunton(on YT) and his colleague, a platform that makes working on/with dynamical systems much easier in terms of sensing, modeling and controlling the dynamics. Eventually iterating quickly using SOTA techniques(compressed sensing, super-resolution(spatial-temporal tradeoff), RandNLA, ML) without the need to understand them deeply
Sensing effectively by optimal (re)positining of the sensors (both number of sensor and physical placement)
Think spatial sensor/data fusion, perhaps using more cheap sensors rather than few expensive
Building a model of the dynamics (based on both reasonable assumption about the system based on grounded physics, and dynamical interpretation of the sensor's data (PySINDy, DMD)
And eventually controlling the dynamics
Love to get your feedback, ideas, if you know about such products/business etc, how could it help any business, real life application, if it interests you, anything :)
A great place to mention Karl Bushby whom is on his way from Punta Arenas, southern Chile to his home in the UK since Nov 1998 by mostly hiking.
He crossed the Darien Gap, the Bering sea walking on ice / glaciers / swimming, as well as swimming the Caspian sea.
In Mongolia he trained to work with camels
Had a lot of issues of course with Russia due to crossing the Bering sea, and maybe the war, as well as covid lockdown in countries, limited days issued for visas etc.
And he just left Turkey and entered Europe! so the only major challenge is swimming the channel.
