#qotojournal — Public Fediverse posts

#introduction #qotojournal #stem #science

Hello there! I'm an incoming #university #student pursuing an MBBS (Bachelors of Medicine and Surgery).

I'm currently working on how corporate #healthcare collaborates with local communities, and improving the healthcare infrastructure in my local area.

I post on various topics i.e. my studies ( #genetics #neuroscience #anatomy #medicine ), hobbies ( #photography #fitness #books ), and whatever interests me :]

It's nice to meet y'all! 🌟

#introduction #qotojournal
I'm one of the many Twitter refugees, mainly known there for my macro pictures of flies and other insects. Using the site waarneming.nl the AI there can identify lots of the insects i've photographed. Last year I've also started to id flies that i've captures under the microscope.

I work as a trainer in programming languages such as Java, Javascript, Python and SQL. I'm also interested in data science and machine learning, reading and writing poetry, singing classical music, walking.

#introduction #qotojournal Hello. I've looked at a couple of servers and finally settled here. What is particularly excellent and unique is the ability to follow another domain of interest and get its whole public timeline on your Home page or in a list. So I can easily keep an eye on what's happening on other servers. Everyone needs Domain Subscription!

#qotojournal
I'm a PhD student obsessed with bio-based nanoparticles, so I'm trying my hand at 3D printing seaweed using at-home supplies. The troubleshooting is intense. Creating an at-home lab without funding is VERY HARD. Mashed seaweed was OK to print (with cellulose nanocrystals), but optimization? Yikes. Would love any tips !

@freemo
Endless appreciations!

This is marvellous! So qoto.org is an entire ecosystem of free apps and mastodon and peertube etc tie into it! This could be excellent for building serious scholarly discussions on the top of #fediverse, and #qotojournal for example builds a knowledge base.

Interesting fact of the day: The same effect that cuased light in a prism to split up into different colors is what ultimately caused the first transatlantic telegraphic wire in 1858 to fail.

Morse code is transmitted as on-off signals, effectively square waves. Square waves are in fact made up of many different frequencies. Like in a prism different frequencies move at different speeds through a wire. Therefore as the on-off pulses traveled through the transatlantic telegraph wire the signal spread out like it does in a prism and ultimately the pulses would overlap and be indistinguishable.

The effect was so extreme that it took a message of only 98 words (the first message sent) over 67 minutes to send one way and a whopping 16 hours to confirm the message.

Whitehouse, a doctor with little mathematical understanding, thought he could solve the problem by increasing voltage, which we now know was a futile effort. He increased the voltage to the point he managed to short out the cable entirely and made it useless. However Lord Kelvin had already warned of the problem as was ignored and he came up with the law of squares to describe the problem which later was refined to give us the telegraphers equation. The telegraphers equation is still used today to model feedlines in radio transmitters and receivers.

#Science #STEM #Physics #History @Science #QOTOJournal

#qotojournal Homemade Udon Noodles
========================
Ingredients

5 cups (600 grams) all-purpose flour, sifted, plus more for dusting
1 tablespoon plus 1 teaspoon kosher salt
1 1/4 to 1 1/2 cups water

Instructions
To make Homemade Udon Noodles:

In a large mixing bowl, combine flour and salt. Add 1 1/4 cups water. Use hands to mix until dough starts to come together in a few large lumps. Firmly press and knead the dough, incorporating any loose flour until there is none left. If necessary, add a little more water, 1 tablespoon at a time, until you can incorporate all of the flour.
Lightly dust work surface with flour. Knead dough (folding and firmly pressing with your palm, folding and pressing forcefully) until dough looks and feels fairly smooth, about 5 minutes. Form dough into ball, wrap in plastic wrap, and let rest at room temperature for 1-5 hours.
On a lightly floured surface with ample room, knead it again for a few minutes. Divide dough into 4 equal-sized balls. Dust each ball with flour and cover with plastic wrap until ready to roll out.
Use rolling pin to roll out the dough, occasionally rotating the dough 90 degrees and lightly using with flour if it threatens to stick to the pin, until just between 1/8" to under 1/4" thick. If the dough is too difficult to roll out, cover with plastic wrap, let rest for 10 minutes, and then resume. This rest allows the gluten to relax and makes it easier to roll out.
Fold the sheet of dough into thirds (like a letter fold) and then slice widthwise into approximately 1/8" thick noodles. Gently separate the noodles and toss them with a little bit of flour, just so they don't stick together. Cook right away.

To Cook Homemade Udon Noodles:

Bring a large pot of water to boil and prepare a large bowl of icy water. Add noodles to boiling water, stirring frequently and adding 1/4 cup fresh water if the water threatens to bubble over, until they are fully cooked but not mushy, 7-12 minutes (depends on how thick your noodles are). Unlike Italian pasta, Japanese noodles shouldn't be al-dente, but don't let them get mushy.)
Drain noodles, transfer to icy water. Briefly and gently rub the noodles with hands to remove some of the starch. Drain from cold water.

2021-03-26, 19:05, Friday

I promised a few paragraphs about x-ray diffraction, so here it goes. This is mostly unedited because I’m tired and lazy.

Basically, light has a property to undergo what’s called diffraction: shine a laser beam on a grated piece of plastic and beam will split into an uneven number of new beams. Using this pattern and some trigonometry you can calculate the wavelength of light if you know how fine the grating is and the angle between beams. This works only when wavelength is a few times smaller than the grating size.

Now, the important bit is that atoms in crystal sort of work like grating. Light reflects from different layers of atoms differently and this forms the same diffraction pattern. Since the distance between atomic layers determines the structure of the crystal, we can now measure it using light and some math called Bragg’s law. The only thing we need is a light source with fixed, well-known and very small wavelength. Now, the “grating” in our case is approximately 2-4*10^-10 m, or 2-5 angstrem.

Conveniently, metallic anode, when put in a vacuum and under high voltage, emits high energy photones, generally of a fixed wavelength, corresponding to the valent electron’s excited state. And if we use copper, this wavelength is roughly 1.51 angstrem, which is about what we need.

Now that all elements are in place, we just need to build a complex machinery that will hold our sample, put a piece of copper under a few kilovolts, cool it down simultaneously, while also rotating a detector to capture light intensities under a range of angles. Different lattices will give different diffraction patterns, and one can be calculated from another.

And this is more or less how x-ray diffraction works.

#qotojournal #science #xrd #materialsciences

@Science

I was asked to explain the space-weather ticker I posted earlier (attached). This was my reply.

ionosphere

First off the are two categories of radio operation that is relevant and effected in opposite ways..

1. space to earth
2. earth-to-earth.

Number 2 breaks down in two ways as well that is

2a. line of sight operation, vs
2b. skywave operation.

Skywave operation is really the most relevant here, line of sight might be effected somewhat by noise floor effects from sun but that is only relevant some of the time.

Reflection Layers

There are two main factors from the sun that affect operation. One is radio interference, this would be caused by flares and ejections directed at earth. In extreme cases it can cause an EMP but thats very rare.

The other is ionizing radiation in the form of UV (a much shorter wavelength version of UV than what reaches the earth), ionized particles, solar wind and similar. This ionizes the ionosphere in a specific way that causes radio waves to be blocked and/or reflected. Basically there are two regions in the ionosphere both in whats called the F-region.. These are F1 which is at 200 km above sea level, and F2 which is at 300 km. The higher F2 layer allows for radio signals at a particular angle to be reflected, this allows radio operators to bounce their signals off this part of the sky and reach distant receivers. Since F2 is much closer to the ground this particular region actually blocks long range communication and thus significantly reduces the distance a radio signal can reach to mostly line of sight.

Usually, when the radiation is high enough, we see the F2 layer ionize first in the morning (basically when the sun is just coming up for people who live near your horizon), which makes the morning the best time to transmit. By afternoon the F1 layer is ionized by sun radiation and thus the signal is blocked again.. the reverse happens in the evening. So early and late day propagation is best. This is called gray-line propagation.

There is also E layer propagation which operates at much lower frequencies and at steeper angles. This is what is used for very short distance transmissions within the 100’s of km. This is called NVIS (Near Vertical Incidents Skywave).

Reading the screencap

Now with this said, it only works when the radiation from the sun is just right. Basically there needs to be enough radiation from the sun to actually fully ionize the layers.

SFI

SFI on the chart stands for “solar flux index” this is a measure of the quantity of ionized particles and solar wind measured. This is usually in the range of 0 - 400 with 0 - 100 being poor for propagation, 0 - 200 being marginal, and 200+ being ideal/good.

SN

SN stands for sunspot numbers, these effect different layers selectively. sunspots reflect the intensity of the sun’s magnetic field. It ranges from about 0 to 400 as well.

Lower values here show a preference to ionize lower levels of our ionosphere. 0 to about 150 will preferentially ionize the E-region and be ideal for low frequency propagation (160m wavelength to 80M) in the NVIS configuration, so very short distance (100’s of km) propagation only which is all these low frequencies can ever do.

Higher values, above 200 means the F-regions are preferentially ionized. That means low frequencies like 160m and 80m will not propagate at all and only work line of sight (10’s of km), but higher frequencies ~20m and higher in frequency will propagate via skywave. These frequencies now can propagate 1000’s of km around the world in these conditions (assuming SFI and other factors are good).

K index

The next line is K-index just labeled K. This one is rather complicated.. it basically looks at the horizontal component of the earth’s magnetic field and how it is disturbed (which is an indirect way of measuring the solar winds and its interaction of the earth).

This doesn’t effect the ionosphere itself so much as the other measures since its only partly effected by solar winds. This is used as a measure of expected band noise and thus how high the noise floor will be. the max value is 9 and indicates significant noise. 5 is about the cutoff where geomagnetic storms are present.

The K-index is not linear and is calculated from the a-index (lowercase a, different from A-index below).

A-index

is really just another way of measuring K-index. Or to be more precise both A-index and K-index are calculated from the underlying a-index (a-index is different from A-index). The A-index is the average of the last 8 a-index, and thus has a much simpler interpretation.

Think of A-index as a long-term rolling average of the K-index in a different scale.

Generally A-index is even less linear than K-index with 0-50 being low noise and 100 - 400 being high noise.. lower is better.

304A

304A stands for “304 Angstroms” which is the wavelength of UV light measures. Basically its the strength of UV radiation from the sun as measured from space (different than earth UV levels). the “@ SEM” part refers to the instrument on the satellite used to record it, called SEM, SOHO and EVE are other possible instruments used to get this measurement and it changes depending on the instrument available at the time.

In this case higher is better as it means more of that F-layer ionization I mentioned.below 80 is poor, 150 and up is good, 250 and up is amazing.

Ptn/Elec Flux

This is Proton and Electron flux. These have a similar effect as UV except they ionize the E-layer more so than the F-layer. So they harm long distance short wavelength propagation but improve short distance long wave-length propagation.

Aurora

This is just the predicted chance of aurora. Not directly relevant for radio.

Aur Lat

This tells us the largest lattitude likely to see the aurora.

Bz and SW

This is the interplanetary (in space) magnetic field vector (B-field means magnetic field). This is the magnetic field that is incoming and striking the earth from space.

The Bz part is the intensity, the SW part is the direction in degrees. When it lines up with the earths magnetic field it strengthens it, when it doesnt it weakens it. Positive values strengthen it, negative weaken it.

other values

Everything else is self explanatory I suspect. “solar flare prob” is the percentage change of a solar flare, which we dont tend to know until just a few minutes before their ejections strike.

“MUF” stands for “Maximum Usable Frequency”. It indicates the highest frequency (shortest wavelength) that is likely to be capable of bouncing off the ionosphere (f-layer or e-layer) and therefore the highest frequency capable of skywave propagation.

#radio #rf #science #space #spaceweather #ham #hamradio #QOTOJournal

For any ruby :ruby: devs out there, wanted to share a neat little open-source :opensource: module I wrote to solve a common problem. Keep in mind ruby is not my main language so if there is a batter approach here I'm all ears.

Basically right now I have a game (text based mud) and in normal and expected fashion objects in the game are created when their objects get initialized, such as a new player being created. The system then periodically saves the universe by marshalling all the object into some serialized format and saves it to a file from time to time. As tends to be the case with serialization, however, when an object is restored, such as a previous player logging out and back in, then the class is created directly without the initialize method getting called , its class and instance variables are simply populated directly.

This is where problems can arise if you change the system and add new features (such as a new variable to an object). New objects that are created will populate the new variable correctly through the initialize method however already existing players will not have that variable set at all (it won't be nil, it simply wont be set, which is a distinctly different state). This is the problem I solved.

What I did was created a mix-in module that lets you set default values for variables, once a class is reinstated from storage it checked if any variables that have defaults are unset (nil variables are considered set) and then applies the default value to them. In this way legacy objects will be able to update to new code changes automatically when it loads. To prevent duplicating code you can even intentionally leave it out of the initialize method and rely on the defaults when it is appropriate to do so.

Moreover the defaults do not have to be static values but can be determined based on the existing state of the object, which makes them dynamic and flexible... a class that uses the mixin could look like this:

require 'defaults'

class Foo
include Defaults

# @bar defaults to @baz*2
default(:bar) { |this| this.baz * 2 }
# @faaboo defaults to 178
default(:faaboo) { 178 }

def initialize
@baz = 13

#this line can be ommited
@bar = 26
# if you add this line instead
load_defaults
#either work fine
end
end

Here is a link to the module:

https://git.qoto.org/aethyr/Aethyr/-/blob/master/lib/aethyr/core/util/defaults.rb

You can see a class that utilizes this new feature here:

https://git.qoto.org/aethyr/Aethyr/-/blob/master/lib/aethyr/core/objects/game_object.rb

#Ruby #RubyLang #Programming #Coding #software #ComputerScience @Science #code #source #sourcecode #opensource #oss #game #gaming #gamedev #QotoJournal

On Publishing And Publishers

TLDR: a quick overview of the situation scientific publishers created and ways to partially solve the problem, or rather bypass it.

Tags: #qotojournal #sciense #publishing #essay

1. Introduction

There is a problem in our world, that is not widely talked about, especially on media. These are scientific journal publishers, or rather the system that they have established a long time ago and keep using (and monetizing) to this day. Don’t get me wrong, I’m all for capitalism, it’s when it gets in the way of important things when I get slightly disappointed.

Let’s agree on the important stuff first: the era of lonely geniuses is long time over. The science and scientific advancements in the 21st century depend on cooperation of dozens, hundreds and thousands of people throughout the centuries. Information obtained years and decades ago is used to build new knowledge upon. A student in my faculty is required to have at least 20 citations in their coursework in the end of the first year, and it’s not an issue - we usually approach 30 mentioned sources and research even more while doing the literature review. This is a bare minimum to become acquainted with the material. Scientific knowledge can be represented as a tree graph, with it’s roots in ancient philosophy and it’s leaves reaching into quantum physics and abstract algebra. To cherish the fruit of this tree, one needs to reach it’s branches or, abstaining from the methaphore, to research everything that has already been done.

The access to this knowledge in the current model is pricey: one article costs between 30 and 50 dollars and journal subscriptions are expensive even for universities. Of course we violate the law and use Sci-hub extensively. This is very wrong, fundamentally wrong: people should be able to do their work legally, especially when this work is to solve humanities’ problems and improve peoples’ lives on the broadest scale possible. And don’t get me started on the trouble it takes to do the fact-checking on all the articles that are on the web. It would have been so much easier to just read the source. Tough luck!

So there is, as I have mentioned, Sci-hub: illegal, but convenient and free way to obtain most of the articles, otherwise paywalled. For downloading textbooks and various books libgen is very handy and very illegal. Sometimes useng orchid or researchgate is possible to contact authors and request a full-text and even ask a few questions, but this is slow and inconvenient; not everyone is on these plaforms, which makes things worse. There is no way to quickly dismantle the “rule” of publishers, the have been around since the beginning of 20th century. They claim to be important because of the peer-review but I am unsure whether the peer-reviewing is that expensive, especially in modern world.

1. Analysis

So let’s list the benefits of scientific publishing. There are some, obviously, and I’m not here to deny them.
1) Peer review. The most important step to keep most of the junk out of the system. Here I refer to pseudoscience and badly written papers as “junk” to save some space.
2) Verification of scientists and institution affiliations. This makes the industry exclusive, but keeps junk out of the system, again.
3) Storing articles and providing a way to access them via identification system (doi).
4) Keeping track of citations.

Now let’s get to downsides. There is plenty, as mentioned above.
1) Paywalls.
2) Very slow system.
3) No way to communicate with authors.
4) Publishers have control over the entire thing.

Now It would be fair to have a look at alternatives and what they can provide. I’m writing this without references so please correct me if something is off.

Sci-hub: free way to bypass paywalls. Solves problem 1, creates legal problem.
Researchgate and orchid: a way to communicate with authors. Solves problem 3 partially.
oaDOI and similar sites: they keep track of open-access articles, this partially and legally solves prolem 1.

That’s it for the most part. It’s not like publishers are going anywhere, as well as doi system. Not in the near future. Maybe we’ll come up with something better than sci-hub, maybe torrent-like system for rticles, who knows. Verification of articles can theoretically be done using blockchain technology. I have no idea what’s next or how to solve the problem, but I am putting Alexandra Elbakyan in my “acknowledgements” list.

Dixi.

Anyone getting failure to open on their desktop MASTODON app?

#qoto #qotojournal @QOTO

@math

Early rough draft, haven't proof read it yet. But just wanted to share the math behind creating an efficient Exponential Moving Average with a finite length/cut off. I found this was needed for a project I am working on where the EMA much be determined by random access to various points in a time series and couldn't be calculated for the entire time series in one go.

I needed to modify the EMA for a finite back-length. The standard EMA is only really efficient when calculated sequentially for the entire time series. This implementation is an efficient design that allows for calculation at a point by using finite back-length.

This is an early draft, did not proof read it yet, just whipped it together, though pretty sure the math is close to the final form. I will publish it sometime tomorrow but want to share what I have and welcome and questions or feedback before I publish it.

https://beta.jeffreyfreeman.me/an-efficient-exponential-moving-average-of-finite-length/

#Programming #Math #Maths #Mathematics #algorithms #Science #QOTOJournal

@Science

A response I gave to a friend who felt the Quantitative Easing used during the COVID epidemic would ultimately hurt everyone, and that an absolutely free market is the only market. While I do agree with free market sentiments the truth is QE does work when used to avoid massive market crashes and as long as it is reversed at a later point once markets recover then it does far more good than bad. Here I lay out all the math to show why.

In an ideal world I do agree that there should never be market interference. But that only works when you truly live up to that at every level, something that you can't truly have and still have a government.

Consider, the only reason we needed to handle a depression at all is because the government first interfered with the market by using law to force shutdown of businesses or their ability to accept customers inside. If the free market were not tampered with in that regard there would have been no significant depression to deal with in the first place. So we are doing cleanup at this point, so the point is somewhat moot.

That said the assessment that interfering now with QE results in more problems later isnt entirely true, only somewhat. You are correct of course that there will be a downward pressure on the market into the future for sometime, which is what you're referring to. But I think its important to understand the exponential nature of money and while earlier short term gains can far outweigh later long term loss.

Basically the dynamic plays out like this when we see QE going down in times of depression.. 1) ROI on market investments soar many times higher than what would normally be possible in the short term. 2) There is more money in the system so far more people are able to leverage that ROI than they otherwise would in the short term. These two principles combined means people see huge financial gains during a time when we would otherwise have huge losses. This is followed by a longer period of time, on the order of 4x to 6x the length of time where we see a boost where the market is now going to underperform, but at a much lower hit than the gain we saw during QE.

So lets use real world numbers to demonstrate the idea with some solid math. I will pull the numbers from the real world numbers and spitball some figures minimally and try to underestimate so as no to bias it in my favor for numbers we don't actually know but that follows the proposed premise of short term gains followed by long term downward pressure from QE that is higher but distributed over a longer time period... Here are the numbers im going with pulled from real world numbers:

Dow Jones industrial average baseline annual return when QE is not evoked, taken from the 20 year average: 7.03%

First lets look at how things would play out if you invested at the start of QE, which was after the COVID lockdowns and the initial covid drawdown (optimal time to invest):

Short term annualized pressure from QE: 93.79% (1)

Short term annualized P&L based on DJ movement (baseline + QE): 100.72%

Long term loss pressure from QE over following 20 years: -74.19% (2)

Long term annualized QE loss pressure: -6.54% (3)

Long term annualized P&L (expected DJ movement, baseline +/- QE loss): 0.49%

Portfolio P&L on 100K invested at beginning of QE after 2 years: +$290,032.80(4)

Portfolio value on 100K invested at beginning of QE after 2 years: $390,032.80 (4)

Portfolio value after 22 years when starting at 100K with QE:  $430,088.72 (4)

Portfolio P&L over 22 years on a 100K investment with QE: +$330,088.72 (4)

Total 22 year percentage P&L with QE: +330.08% (4)

Annualized percentage P&L over 22 years with QE: +6.85% (4)

Time to DJI recovery with QE (actual observed): 275 days (0.75 years)

As you can see the gains are still quite nice even with QE slowing the market down to a crawl for 20 years. Just for comparison lets run the same numbers without QE doing our best to use the real world figures again.

Observed drawdown loss due to covid with QE: -36.77% (5)

Projected drawdown loss due to covid without QE: -85.58% (6)

Historic annualized recovery rate with QE (2009 - 2013): 18.82% (7)

Historic post-QE annualized P&L (2013 - 2018): +12.63% (7)

As you can see, even post QE for the almost 5 years that followed we saw an annualized **increase** in ROI over the baseline of 7.02% and not the decrease you propose or expected.

Time for covid recovery without QE: 10,418 days or 28.5 years (8)

So we can see the time to recover your pre-covid investments without QE is only 275 days compared to without QE we are talking 10,418, or over 28.5 years. Thats huge. So more importantly what would a pre-covid portfolio look like in 22 years with and without QE. Remember the above portfolio numbers were if you invest at the beginning of QE, an optimal point, not pre-covid drawdown, which is the least favorable point.

Loss on pre-drawdown 100K at minima with QE: -$36,770 (9)

Portfolio value on pre-drawdown 100K at minima with QE: $63,230 (10)

Portfolio value on pre-drawdown 100K after 22 years with QE: $271,939.58 (11)

Gain on 100K due to covid drawdown with QE: +$171,939.58 (12)

Now for the numbers without QE

Loss on 100K due to drawdown without QE: -$85,580 (13)

Portfolio value on pre-drawdown 100K at drawdown without QE: $14,420 (14)

Portfolio value on pre-drawdown 100K after 22 years without QE: $62,594.80 (15)

Portfolio P&L on pre-drawdown 100K after 22 years without QE: -$37,405.20 (15)

Portfolio percentage on pre-drawdown 100K after 22 years without QE: 62.59% (16)

Portfolio P&L percentage on pre-drawdown 100K after 22 years without QE: -37.4% (16)

Portfolio P&L on pre-drawdown 100K annualized from 22 years without QE: -2.1% (17)

(see notes at bottom to see how these were calculated from the real world numbers)

As you can see under a QE scheme we recover in a fraction of the time to baseline, only takes us three quarter of a year before we are back to where you started and after 22 years you've still managed to turn your original investment into 4.3x its value in 22 years, and gaining just a little below base rate per year on average at 6.85% gain annualized. By comparison without QE your original investment would actually have lost value over a 22 year period being reduced by almost a half with portfolio investment dropping by -37.4% of its starting value and with an annualized average return over that time of about half at -2.1% loss per year. So clearly QE applied early on before a recession has a chance to fully realize, even with a theoretical economic hit that applies in the 20 years that follows (though we have not observed this historically), is more profitable for everyone than it is to just let the market naturally recover.

This is, in fact, as shown, demonstrated by historic values which agree with currently observed and calculated values here. In fact I was ultra conservative and the real numbers are likely far better. QE tends to cause recoveries proportional to the rate at which the QE occurs followed by an increase in future gains rather than a decrease for at least half a decade out. In fact even when we reversed the QE from 2018 to 2020 there was no noticeable hit to the economy which was still growing in part due to the fact that it was thriving from the effects of earlier QE in past recessions.

This doesn't mean QE works as a general principle outside of extreme circumstances such as a significant recession. It will effectively weaken the dollar when it comes to foreign trade as a direct result, which is reversible through reverse QE. We can see that with the current QE, the effect is relatively quick.

Pre-covid/lockdown the dollar had a value of 0.93 relative to the euro, during the economic crash of covid, but before QE was initiated the dollar crashed from 0.93 down to 0.88 relative to the euro at the point QE was initiated. QE had the initial effect of raising the dollar in the short term from 0.88 to ~0.935 but as WE progressed the dollar ultimately crashed back down from QE to 0.845. As such it went from 0.88 to 0.845 due to QE with only a temporary value boost of a few months. Essentially it reduced the value of the dollar by 4%. This value, as with past QE is realized very shortly after QE ends and stabilizes flat without any further reduction in the dollar due to QE after it ends. So while it does negatively impact the dollar, the impact is not huge. Moreover since it significantly bolsters local economic growth both long term and short term as a result ultimately the QE will/can be reversed at a later date without economic impact and the value of the dollar will gain back that 4% plus whatever value it gained as the result of the boosted economy.

So while its effect on the dollar would cause us to discourage its use in non-recession periods, and it is a tactic that has diminishing returns if used in that way, so not effective. Overall when used during recessions it is an effective tactic that overall reduces the fallout from a recession significantly and boosts economic recovery overall, rather than harming it.

==== Notes if you want the math ====

(1) to calculate the annualized gain from QE is simple. There was a 60.6% rise in the DJ from march 23rd until today, that's 248 days. The following equation will turn that into this year's annualized DJ gain: daily return = 1.606^(1/248), yearly return = (1.606^(1/248))^365. So we have a daily return of 0.19% daily or 100.82% yearly ROI. Now we already know that the baseline ROI is only 7.03%, so we can therefore calculate that at the current rate of annualized gain is 100.82-7.03 = 93.79% **over baseline**

(2) For this we don't know the actual number but we can estimate based on first principles. Let's assume that the negative effect of QE is twice that of the annual positive effect, but spread over 20 years rather than condensed into a short period like positive effects are. In terms of percentage that would be: 1 - 1/(1.937*2) = -0.7419, so basically there is an upward pressure in year one of ~93%, representing a near doubling (100% or 2x), and then a downward pressure of -74.19% the following 20 years which represents a quartering (1/4th or 0.25x) over the 20 years to follow.

(3) to go from the 20 year figure to the annualized figure we have the following equation: 1 - (1/(1.937*2))^(1/20) giving us a annualized loss pressure due to QE of -6.54%

(4) For this we have to determine how long the positive effects of QE last before the proposed negative pressure kicks in. It is hard to say for sure and we would have to do our best guessing based on historical examples of QE. What we know is the returns from QE follow a logistic curve (similar to a logarithm but restricted such that it will have a maximum attainable maximum value before leveling off). As such the initial gains you see will be sharp and appear linear at first and at about half way through you will start to see the gains slowly taper until finally leveling off. Right now we can see from the current DJI market movement we are still in the early part of seeing the GAINS from QE and must not even be half way through. So assuming the gains will last at least 2 or 3 more months before the tapering is noticeable, indicating we are about half way through the gains then a reasonable estimate is we should see these gains for at least a total of 2 years since the start of QE before the gains dissolve and a downward pressure begins to be applied instead.

Therefore assuming we see QE gains at observed rates for only 2 years before transitioning to the negative effects lets calculate what that is. We know annualized gains due to QE during the positive phase is 93.79% above baseline, taking that to a 2 year figure is:

(1.9379)^2 -1 = 2.755 or +275.5%

Add in the baseline rate to arrive at the 2 year gains during the positive period of QE:

2.755 + ((1.0702)^2 - 1) = 2.90032804 or +290.03%

Now to calculate our portfolio value n 100K after 2 years:

(100000 * 3.90032804) = $390,032.80

Subtract 100K to see profit value:

(100000 * 3.90032804) - 100000 = +$290,032.80

Now lets calculate the portfolio value after 20 more years of QE negative pressure, this would result in previously stated annualized P&L for the next 20 years of  0.49%.

390,032.80 * (1.0049^20) = $430,088.72

and subtract initial investment to see what that is in P&L:

390,032.80 * (1.0049^20) - 100000 = $330,088.722

The P&L as a percentage is simple now:

(430088.72/100000) - 1 = 3.3008872 or +330.08%

Lets get that to its annualized value:

4.3008872^(1/22) - 1 = 0.06855798324 or 6.85% annualized return

(5) This is the actual observed drawdown due to COVID before QE was employed, after which the stock market started to rise. Keep in mind QE was applied before covid has completely run its course so in reality we would have likely seen this trend continue throughout the lockdown period at a minimum and probably longer. The loss during max drawdown observed due to covid was from ~february 20th to march 23rd, only a 31 day period!

(6) To project the overall loss we first observe the initial downward spike was linear at a constant rate more or less. We can project that out to what it would look like if allowed to continue into the end of lockdown without intervention to artificially reverse it. Covid restrictions began easing across most states by late may and lockdown was over by late june and into july, though restrictions remain in effect for non-essential employees going back to work still by then. Being conservative so the numbers don't work in favor of my argument let's just assume the projected covid loss would have gone to the end of lockdown then the loss would have abruptly ended with a reversal and recovery. So from February 20th to July 1st which is 131 days would have been the natural drawdown period.

first with take the -36.77% loss over the observed 31 day period and turn that into our daily rate:

1 - (1-0.3677)^(1/31) = 0.01467 or -1.467% daily loss

Then we take our daily loss and apply it to a 131 day period:

1 - (((1-0.3677)^(1/31))^131) = 0.8558748085 or -85.58% total drawdown

(7) Next let's take a look at an example of QE in recent history, we used QE extensively from 2009 to 2013 for our economic recovery from the 2008 recession. This is also the most comparable drawdown to the COVID drawdown we have. The recession began 1st of october 2007 and reached its bottom point on the 2nd of february 2009 with a total drawdown of 49.43% over 490 days. The DJI recovered to the value it was at on oct 1, 2007 for the first time since then on January 15, 2013, or 1443 days.

QE began to be employed only one month prior to bottoming out from 2009 recession and was the reason we began to see recovery at this point. However in this case the QE applied was spread out over a 4 year period very slowly, as such the recovery took much longer and the damage done was much greater. In fact the total quantity of QE due to COVID is almost exactly the same as it was when applied to the 2009 recession except for two important details 1) the QE was applied over a 4 year period instead of a few months 2) Trump spent 2 years prior to 2020 from 2018 to the end of 1029 **reducing** the QE from the Obama era by 22% before employing QE again. This means that overall the total QE is in fact less than the QE used to recover from the 2009 recession but applied over a much shorter period. This is absolutely key to the success, if you are going to take a hit from QE you want the gains to be concentrated as much up front as possible to have the best long term gains. Spreading out the QE ultimately caused a lot of harm.

Still we can ue 2009 QE as a historic example of how QE can be successful, even long term, and we can also use it a bit later as a comparison against the current QE to get a sense of just how much better performance we get out of QE concentrated up front as opposed to being spread out. So lets take a minute to look at what sort of numbers we see from this.

Considering the projected drawdown due to covid is significantly higher than the earlier recession, which is understandable considering it literally halted all business everywhere, we would expect a similarly long recovery, in fact even more so since covid didn't just go away on that date, economies around the world are still crashing. What you notice if you look at the recovery post 2009 is that the market movement went higher than baseline expectations of growth at the usual 8 some percent when not in a recession though after the recovery it went back to about baseline. In fact we never really noticed much of an economic hit long term from the QE at all as you might expect as it was overshadowed by the gains of capital injection. Now covid as I stated would have likely had far more significant long term effects and and the increased rate of recover over baseline from 2009 is from the QE, a natural recession will recover at below baseline for some time before eventually recovering to a baseline recovery rate. But lets see what the historic QE based recovery rate is based on 2009's QE.

First lets calculate the percentage needed for the DJI to recover to baseline once the recession has reaching its minima:

1/(1-0.4943) - 1 = 0.97745699031 or 97.74% needed to recover from -49.43% loss

this was over 1,443 days (3.95 years) so the daily percent return seen during the recovery was:

(1.9774)^(1/1443) - 1 = 0.00047258762 or 0.0472% daily

To annualize that:

((1.0004725)^365) - 1 = 0.18817886404 or 18.82% annualized gains

That is, indeed, quite a bit above the usual 7% - 8% we calculated outside of QE. But was there a long term hit? Well lets look at the growth rate once QE ended which was beginning of april 2013 until 2018, which is when Trump began reversing the QE.

From april 1 2013 to Jan 1 2018 the DJI increased a total of 76.21% over 1,736 days or 4.76 years. We can annualize that figure and get:

(1.7621^(1/4.76)) - 1 = 0.12638560229 or +12.63%

(8) Now let's calculate the projected recovery time from COVID given a natural baseline recovery rate without QE, which we stated earlier was 7.02%. Again remember this is extremely conservative and real numbers are likely to be below baseline during recovery until we recover and only then would baseline growth be realized again. But I want conservative results that are not favorable to my assertion to prove my point.

First let's calculate the total percentage needed to recover from the covid drawdown of -85.58%:

1/(1-0.8558) - 1 = 5.93481276006 or 593.48% (yes it is that huge!)

Now assuming post-covid lockdown we have a baseline recovery rate with an annualized P&L of 7.02% how long would that take in order to compound to the point we would have the needed total percentage of gain to equal the above figure?

First we calculate the daily percentage gain off the 7.02% baseline annualized figure.

1.0702^(1/365) = 1.00018589549 or +0.01858% daily

Now lets take that and use it to figure the number of days needed to recover to pre-covid DJI:

(1.00018589549)^days - 1 = 5.9348
(1.00018589549)^days = 6.9348
log(6.9348) / log(1.00018589549) = 10,418.39 days or a whopping 28.5 years

(9) This is easy as we know the covid draw down of -36.77%

100000 * -0.3677 = -36,770

(10) to get total portfolio value just sum our losses and our starting value

100000 - 36770 = $63,230

(11) We know from earlier that if you had invested at the bottom of the drawdown with QE applied as it is and held for 22 years we would have the earlier stated gain of +330.08%. Therefore now that we know the amount we would have held at that dip if we invested pre-covid we simply apply that to the value to see our portfolio value after 22 years:

63230 * 4.3008 = $271,939.58

(12) for this we just subtract our initial 100K investment:

63230 * 4.3008 - 100000 = +$171,939.58

(13) We already know we have a -85.58% projected drawdown without QE so we get

100000 * -0.8558 = -$85,580

(14) subtract our loss from the last step from our initial investment to see the portfolio value at the bottom of drawdown:

100000 - 85580 = $14,420

(15) Since we know the baseline annualized growth under free market conditions is 7.02% on average, and we know the amount of money we would have on  100K investment at the minima of the drawdown it is trivial to calculate the amount we would have after 22 years.

Since we would hit the minima after 132 days (the length of time of the drawdown) then we subtract this from the number of days in 22 years (8030) and that is the amount of time we spend in the recovery phase.

8030 - 132 = 7898 days or 21.64 years

An annualized return of 7.02% reduced to a daily return is:

1.0702^(1/365) - 1 = 0.00018589549 or +0.0185% daily profit

Apply this across 7898 days:

1.00018589549^7898 - 1 = 3.34083241673 or +334.08%

Now apply this percentage to $14,420 and it will give us our total portfolio value after 22 years

14420 * (1.00018589549^7898) = $62,594.80

The P&L figure is just this value minus initial investment

62594.80 - 100000 = -$37,405.20 (a loss even after 22 years)

(16) As a percentage our 22 year portfolio value is:

62594.80 / 100000 = 0.6259 or 62.59%

Minus 100% to see that figure as a P&L figure: -37.4%

(17) Finally lets turn that into an annualized P&L as a percentage

0.6259^(1/22) - 1 = -0.02107 or -2.1% annually

@General #Science #Economics #UsPol #Math #Maths #Mathematics #QOTOJournal #QE #QuantitativeEasing #Trump #COVID #COVID19

Physics Nobel Awarded for Black Hole Breakthroughs | Quanta Magazine
https://www.quantamagazine.org/physics-nobel-awarded-for-black-hole-breakthroughs-20201006/

#physics #Nobel2020 #nobelprize #qotojournal

FYI to all my Electronics people out there.

Did you know you can represent capacitance and inductance with a complex number and by doing so you would incorporate ESR (equivalent series resistance) into your equations. Essentially you dont have to model with idealized components and add in the parasitics manually, you can add the parasitics directly into the capacitance or inductance of your components!

See the following equation in my blog and the subsequent explanation if you want some further details and examples: http://jeffreyfreeman.me/an-indepth-look-at-duals-and-their-circuits/#mjx-eqn-realcap

#electronics #electricalengineering #EE #HamRadio #AmaetureRadio #Physics #Science #QotoJournal

For those of you who enjoyed my recent blog post on Circuit Duals and Magnetic Circuits I have now compiled a version of it in PDF if you want to keep a local copy or have it for reading on on an e-reader. Feel free to distribute the link.

https://drive.google.com/file/d/1fECh_IeFYBHoFuaw08zO2dkluZPunkP7/view?usp=sharing

For those of you who are just now hearing about it the article describes the idea of Duality in math, how it applies to circuits, how to calculate duals for systems of equations, and a few examples of circuit duals. It also goes indepth on magnetic circuits as a dual of electric circuits. There is an interesting blurb at the end about how to extract energy from permanent magnets as well.

If you prefer the more colorful blog link instead, which will also be the only place I make corrections or update it most likely, the link for that is as follows:

http://jeffreyfreeman.me/an-indepth-look-at-duals-and-their-circuits/

#Electronics #EE ##RF #HamRadio #AmateurRadio #Science #Physics #Math #Maths #mathematics #qotojournal