Mad Science #4: Geo-Engineering With Nukes

Nuclear devices, what are they good for?  Almost nothing, it turns out.   They’re close to useless as weapons, since the goal of war is domination, not destruction.  The nuclear powers have been in dozens of wars since 1945, and have never come all that close to using them.   They make too much of a mess and cause too much auxiliary trouble.

So there must be something else that one could do with this expensive tech.   The Soviets sure tried.  They had a huge program called Nuclear Explosions for the National Economy, which did 156 tests between 1965 and 1989.  They tried fracturing rock for oil and gas only to find that it became radioactive.   They tried to create underground caverns for the storage of oil and gas, and for nuclear waste itself, but the caverns were unstable. They used nukes to blow out gas well fires, which actually does work but contaminates the field.

But the most interesting usage was for mega-scale civil engineering, projects that could affect the planet’s balance.   The one that actually got started was the Taiga Project of 1971, an attempt to dig a canal between the Kama and the Pechora rivers.  The result is still there:

The 600 x 400m crater left by the three Taiga Tests.  Photo taken from a paper on its current radioactivity. Click for source

The Pechora flows into the Arctic Ocean, while the Kama joins the Volga and then flows into the Caspian Sea.    There is lots of irrigation around the Volga that could use more water, and the Caspian itself is land-locked, and so in danger of drying up.  The Pechora is a major river, with 1/4 of the discharge of the Mississippi at its mouth, and 1/2 of the volume of the Volga itself.  Rather than waste all that water on the useless Arctic ocean, why not send it south?

Pechora-Kama Canal Map

The land between the rivers is relatively flat, and has long been used as a portage.  A canal had been proposed back in the 1930s, but to move serious amounts of water a really big channel would be needed, and it couldn’t have locks.   The total distance was about 100 km, but the southern 40 km was flat enough that it could be dug by conventional means.  The northernmost 60 km had a range of hills of up to 60 m high, so that’s what needed the nukes.  They would use them to excavate down about 80 m to make a channel with a cross-sectional area of 2000 m2.   That would be 20 m deep and 100 m wide if rectangular, but it would actually be more like a triangle.

A researcher at Lawrence Livermore National Laboratories, Milo Nordyke, did an analysis of the project in 1979: Estimates of the Nuclear Design Requirement for the Pechora-Kama Canal Project.  Nordyke had been involved in the US peaceful nuke program, Operation Plowshare, as in beating swords into.  It showed rather un-American timidity and only set off 27 tests between 1961 and 1973, but was stopped by quite American local opposition.

The canal looked quite feasible to Nordyke, but would need at least 250 devices, of up to 150 kilotons each.   The actual test used three devices of 15 kT each.   They used very small fission igniters, of only 0.3 kT each, to reduce the amount of fission products.  They were set off 150 m underground, also to keep the radiation down.

That failed.  The 2009 study mentioned in the top picture found that the radiation around the lake peaked at almost 1000 times the background.  It included lots of radioactive isotopes like Cesium-137, Cobalt-60, and Americium-241.  The site is surrounded by a fence, but people fish in it anyway.

Great.  Just this small test has contaminated the area, although it appears to be far from any settlements.   It’s not as bad as the Polygon in Kazahkstan, an 18,000 km2 area that was permanently poisoned by 456 Soviet nuclear tests, but it’s still bad.

What really puts this in the Mad category, though, is the overall size of the project – 250 bombs.   This was in 1971, when people already knew quite a lot about contamination.   The water flowing through the canal would have poisoned a good fraction of Russia’s agricultural land via irrigation.   All of the peaceful tests had the same problem – more radiation got out than expected.  Even small tests caused trouble, so setting off hundreds of them was ridiculous.

Yet the project had an unexpectedly positive side-effect – it drove DARPA to start research into climate modeling.   Sharon Weinberger discovered this as part of  her history of DARPA, The Imagineers of War.  She writes about it in Chain Reaction – How a Soviet A-bomb Test Led the US Into Climate Science.  The Soviets had been talking about re-routing rivers for a long time, and then in 1971 they actually started doing it.  The head of DARPA at the time, Stephen Lukasik, had the entirely proper reaction: “Holy shit, this is dangerous!”

If fresh water stops flowing into the Arctic, what effect does that have on global climate?  The planet’s ocean currents are not just driven by temperature differences, but also by density changes due to salinity.  That’s why people are so worried today about fresh meltwater from Greenland shutting down the Gulf Stream.  If the Arctic Ocean becomes more saline, what happens?

No one knew.  Lukasik assigned a young Air Force meteorologist, John Perry, to find out.  He got $4 million to distribute to studies of paleo-climates and computer modeling.  That became a lifeline for the Illiac IV, the first big multi-processor supercomputer, and kicked off lots of climate projects.    In 1976 it was taken over by NOAA and the NSF, and morphed into the current US federal climate program.

So a terrible but typical bit of Soviet hubris prompted a research program into what has become the major environmental issue of the age!  I hope the irradiated fishermen of the Taiga Atomic Lake don’t mind.

 

 

 

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Who Is the Most Corrupt US Businessman?

Paul Waldman of the Washington Post was recently writing about the raid on the office of Donald Trump’s fixer, Michael Cohen, which is likely to uncover lots of shady dealings.  Waldman wrote “He [Trump] may well be the single most corrupt major business figure in the United States of America.”

That sounds like a challenge!   Can we find a US business figure even more corrupt than Trump?   There are a lot of unpopular businessmen that I could include here, like the Koch brothers and the Coors family, but they’re unpopular more because of their heinous politics rather than outright crimes.  So here are some sleazier candidates, in alphabetical order:

Sheldon Adelson – casino magnate.  He has been credibly accused of bribing Chinese officials to set up casinos in Macau, and of running prostitution rings out of them.   He also bought a great deal of attention from the GOP (he was Trump’s largest single contributor), including the planned move of the US embassy from Tel Aviv to Jerusalem.  This is a clear disaster for the US, getting it far more deeply into the hole that Israel is digging for itself.  On the other hand, running hookers is not as bad as actually harassing women, as Trump  has.

Richard DeVos – co-founder of Amway, the world’s largest multi-level marketing  (MLM) company.  The less polite name is pyramid scheme.  They claim to have 3 million “independent business operators”.  Hardly any of them make any money, but they pay commissions to their “upline” recruiters.   They’re also encouraged to buy sales materials from their uplines, which can be a large part of their profit.  DeVos himself is worth $8B.  He got in on MLM right from its beginning by selling Nutrilite health supplements from the California Vitamin company in 1949.  It had pioneered MLM in 1945, and he later bought it.  The FDA shut that down as false advertising, but they branched out to a lot of other products, hardly any of which are distinctive.  His daughter-in-law Betsy DeVos is now Secretary of Education, probably for her GOP contributions and efforts to undermine public schools in Michigan.  Trump is likely to have followed DeVos’s lead when setting up Trump University, but he only ripped off a small fraction of the people that DeVos has.

Robert Durst – heir to a real-estate fortune, and now on trial for murder in California.  He’s accused of killing a friend, Susan Berman, and is also suspected in the death of his wife, a neighbor, and three teenage girls.   He was the subject of a six-part HBO documentary, The Jinx, and appears to have confessed on camera when he didn’t think the mike was on.  This is way worse than anything Trump has done, but Durst does not appear to actually be a businessman – his brother Douglas runs the empire.

Bernard Madoff – runner of the largest Ponzi scheme in history with a peak claimed value of $64B in 2008, when he confessed.   Investors chipped in about $20B, and about $11B has been returned by the liquidators, so the total real loss is about $10B.  The assets of Madoff and his family were sold off long ago, and were nowhere near that amount, so someone still did very well out of all this.  It’s probably overseas banks that will just seize the abandoned accounts.  Still, victims who invested less than $1M with him got full restitution, so the main losses were with already rich people.

Angelo Mozilo – CEO of Countrywide Financial when it was a major contributor to the sub-prime mortgage catastrophe.  At its peak in 2006 it issued 17% of all the mortgages in the country.  Most of them had adjustable rates and no documentation.  That meant they couldn’t be backed up by the US housing guarantors Fannie Mae and Freddie Mac, so they were bundled together into mortgage securities and used as collateral on derivatives.  When housing prices fell in 2007 and the rates were reset, massive numbers started defaulting, and the dominoes toppled.  The company collapsed in late 2007 and was bought for nothing by Bank of America in 2008.    While Mozilo was engineering all of this, he was cashing out his own stock for about $300M.   The SEC later charged him with bank fraud, and he had to return about $50M.  Boo hoo.  At least he can never work in a public company again.

Compared to these people, Trump comes off as a piker.  Sure, he’s ripped off a lot of contractors and investors, but that’s more in the several hundred million range rather than many billions.  Sure, he’s made a fair amount of money in fees for managing things badly and in laundering oligarch money via real estate, but that’s also more in the several hundred million range.  Yes, he has embarrassed and humiliated dozens of women, including his wives past and present, but hasn’t actually injured anyone, as far as we know.

No, Trump’s opportunity to do big damage is right now.  He has already harmed Puerto Rico by botching the cleanup from Hurricane Maria, and has enabled the brutal Saudi war in Yemen, which has killed tens of thousands.  As a businessman, he didn’t have the opportunity to harm as many people as those listed above, but as president, he can far exceed them.  He’ll be the biggest yet!

 

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Google Is Getting Creepy

Facebook has always been creepy, of course, with its reliance on selling your personality to advertisers.  People are shocked, shocked, that this would be used for political ends by Cambridge Analytica and other manipulators, but that’s basic to what they do.

But Google has been getting creepier too with time, and not just because they also try to infer your preferences from your search choices.   They’re also using the new tech of machine learning to do creepy things.

I saw this directly at a talk last month by  Olivier Temam of Google Paris called “A Shift Towards Edge Machine-Learning Processing”.    This was at ISSCC 2018, and the slides are here  and abstract here.  The talk started by describing the recent successes of machine learning, and those are impressive and uncontroversial.   It has now gotten quite good at difficult tasks like language translation and image recognition, even of things like cancer cells.  The rest was about how to do machine learning on small systems, ones that could go into gadgets, instead of having to communicate with huge servers in distant buildings.   These need interesting hardware techniques to run fast and at low power, and are now the subject of massive research efforts.

But they’re getting applied to hackle-raising things.   Temam talked about occupancy detection for offices, where a camera tries to count the number of people in a room in order to control the temperature and ventilation.   They want to do the counting in the camera itself for “privacy reasons”, so that the whole video stream does not get uploaded to some server.   But who would believe that it isn’t being uploaded?   Or that the camera isn’t looking at you or your screen to monitor what you’re doing?  This kind of counting can be done much more easily and cheaply with an infrared sensor, with no such privacy concerns.

Then there’s Google Clips, a new camera they’ve developed that can run their machine learning package:It uses a brilliant new chip called the Movidius Myriad 2, now owned by Intel, that can do huge amounts of work at low power.  It has 16 GB of internal storage, but links wirelessly to your phone to upload everything.

So do they have it cleaning up pictures, allowing you to get the best shot no matter what the lighting?   No, they want it to take the video, not you.  They got a team of professional photographers to work with a crew of babies and pets.   They captured the entire video stream from their cameras, and looked at when the pros actually pressed the shutter button to capture a clip.  Then they set their neural nets to work on the stream, trying to determine just what the cutest moments were.   Should it capture when the baby is facing you?  The net detects a large round blob in the middle of the image.   When it’s smiling?  When it’s raising its arms in glee?   When it’s rolling over?  The net doesn’t care – it’s just trying to predict when the professional would push the button.  It knows when the actual push happened, and adjusts the synaptic weights on all of the filters it runs on the images to generate features that map to cuteness.

As Elon Musk said “This doesn’t even *seem* innocent.”  This widget is watching and judging your baby constantly.   It’s assuming that you’re too busy or stupid to film your own baby.  God knows what it actually does with the video, but somewhere a Facebook type is thinking about how to monetize your baby videos.

OK, but creepier still is their AIY camera kit:This contains a lens, image sensor, button, and a cardboard box for the body.  You supply a Raspberry Pi processing board, and load their software onto it.   The demo is, and I’m not kidding, a joyfulness detector.   You point it at someone’s face, and it gives you a measure of how joyful their expression is.  An LED turns yellow for joy and blue for sad, just like the emotions in the Pixar “Inside Out” movie.    “And if your joy score exceeds 85%, an 8-bit sound will play. Cool!”  That’s one reaction, but not the one I would have.

This is still all kind of minor, though.   Where this attitude starts to matter is in their self-driving cars.  For the last ten years they’ve been saying how wonderful it will be when driving is taken away from fallible humans.   30,000 people a year are killed on the road in the US!   If you’re skeptical about this, you’re some Luddite delaying the self-driving millenium, and costing thousands of lives in the meantime.  Cars shouldn’t even have steering wheels!   Trust the machine!   Put your lives in our hands!

I would believe more of this if Google (now spun off into Waymo) were actually selling car safety systems.  They’ve spent billions on this by now, but haven’t offered a single product.  Real car companies are steadily adding safety features: blind spot detection, back-up collision alerts, and automatic forward braking.  I have them on my 2017 Chevy Volt, since they really do make a difference in accident rates.  I find the braking to be annoying, to be honest, since the alert goes off constantly in harmless situations, and every few months it applies the brakes when it shouldn’t.  But Google isn’t doing any of this.

I think the reason is money.   The Volt’s collision detector is based on a camera built by an Israeli company called Mobileye:

A Mobileye camera and processor, usually built into the back of the rear view mirror

They were acquired by Intel in 2017 for $15B, but in 2016 they sold about 6 million systems for $400 million.   That’s terrific for a small company, but chump change to Google.  Even if they sold ten times as many systems, 60 million a year, enough for 75% of the cars built each year, that’s still only $4 billion.  Google makes over a $100 billion a year.

No, serious money in self-driving cars only comes when they can sell car-when-you-want-it subscriptions.  Charge $500 per month, and have one car handle four or five subscribers, since each one only uses it for an hour or two a day.  Now you’re making $25K / car / year.   Run a million of those and it’s $25 billion.   When the tech really works, run 10 million of them, and you’re making $250 billion.

That’s what this is about, not safety.  It’s certainly what Uber is going for, since they’re presently losing money on every ride.  Actually doing full autonomous driving (called Level 5) is an enormously difficult problem because the driving environment is really ill-defined.   Just because Google’s Alpha Go program can beat a human champion doesn’t mean it handle situations without fixed rules.  No one cares if it makes a bad Go move, but people care a lot when your software kills someone.  The current accident rate in the US is about one fatality per hundred million miles, or several million hours.  It’s extraordinary hubris to think that computers can do this a lot better, and that hubris is going to kill people.

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Who Are the Best-Selling SF Authors?

There doesn’t seem to be a direct way to answer this.   Book sales data appears to be kept very private between authors and publishers, probably for the same reason that you never tell anyone your income.  In any case, books are a highly durable product and can last for centuries, so knowing modern sales figures wouldn’t say much about how many books were sold long ago.

But maybe we can answer this a different way.  The website LibraryThing lets you keep a catalog of your own library online.  It currently has 2.2M members, and 122M works cataloged, representing 11.7M unique titles.  I use it myself.   It can show the number of works held by its members by author.   This can tell us how popular authors are, at least among these bibliophilic and technophilic users.   They’re far from a random sample of readers, but they’re probably more similar to you, if you’re reading this blog post.

The most popular author by this standard is J. K. Rowling, who has 625,782 works in the collection as of this writing.  That’s 0.5% of all the books listed!   For other authors, let’s express their popularity as a percentage of hers, rather than by somewhat meaningless raw copy counts.   The webpages also show which individual book has the most copies, so let’s also look at whether that book dominates the author’s total.  It even shows the total number of works held, although that can include a lot of really minor stuff.

I sampled a lot of authors in this spreadsheet: LibraryThing Author Statistics.  Many of them write in multiple genres, but I assigned them to the genre of their biggest book. I did make an exception for Ursula K. Le Guin, because I’m a fan.   Below is how it looks for the top 20 SF authors.  Click on the link to see the author’s full list on LibraryThing:

Author Lived % of Rowling’s copies Book with Most Copies % of author’s total Number works
Isaac Asimov 1920–1992 29.6% Foundation 7.6% 1901
Orson Scott Card 1951– 23.8% Ender’s Game 20.8% 340
Anne McCaffrey 1926–2011 23.7% Dragonflight 4.1% 262
Kurt Vonnegut 1922–2007 22.2% Slaughterhouse-Five 23.7% 227
George Orwell 1903–1950 21.4% 1984 43.3% 266
Douglas Adams 1952–2001 21.4% The Hitchhiker’s Guide to the Galaxy 20.9% 110
Robert A. Heinlein 1907–1988 20.2% Starship Troopers 7.1% 341
Margaret Atwood 1939– 19.0% The Handmaid’s Tale 22.7% 187
Ray Bradbury 1920–2012 16.2% Fahrenheit 451 35.6% 803
Ursula K. Le Guin 1929–2018 14.9% A Wizard of Earthsea (The Earthsea Cycle, Book 1) 10.8% 397
Philip K. Dick 1928–1982 14.7% Do Androids Dream of Electric Sheep? 14.1% 525
Frank Herbert 1920–1986 13.4% Dune 31.0% 178
Arthur C. Clarke 1917–2008 13.4% 2001: A Space Odyssey 10.7% 482
Neal Stephenson 1959– 13.0% Snow Crash 18.5% 70
Larry Niven 1938– 11.0% Ringworld 10.1% 299
Aldous Huxley 1894–1963 10.6% Brave New World 59.1% 234
William Gibson 1948– 10.6% Neuromancer 25.8% 51
Iain M. Banks 1954–2013 10.2% Consider Phlebas 7.8% 54
H. G. Wells 1866–1946 9.8% The Time Machine 19.7% 898

Asimov wins! And he’s not just known for Foundation. And there are an enormous number of works under his name, 1901, which is unsurprising given that he wrote over 500 full books.  The authors with the most works are him, Wells, Bradbury, Dick, and Le Guin, who all had long, productive careers.

Orson Scott Card and Ann McCaffrey come in at #2 and #3, which higher than I would have expected.  Likewise Heinlein at #6 and Clarke at #12 are lower.  I’m pleased that Iain M. Banks made it onto the list, and if you added in his non-SF work (published as just Iain Banks), that would add another 3%.

Orwell, Bradbury and Huxley are mainly known for one work, but those works are major.  McCaffrey, Heinlein, and Asimov had the lowest percentages for their biggest book, showing what diverse output they had.

There are only a few living authors (although we just lost Le Guin!), and only three women, so this represents an older view of the field.  This might well be an older audience, one that has had time to build up enough of a library to want to catalog.

For comparison, let’s look at the top 10 genre authors:

Author Lived % of Rowling’s copies Book with Most Copies % of author’s total Number works
J. K. Rowling 1965– 100.0% Harry Potter and the Philosopher’s Stone 14.9% 177
Stephen King 1947– 77.6% The Gunslinger 3.3% 664
Terry Pratchett 1948–2015 61.1% Good Omens: The Nice and Accurate Prophecies of Agnes Nutter, Witch 6.3% 312
J. R. R. Tolkien 1892–1973 48.3% The Hobbit; or There and Back Again 21.5% 620
C. S. Lewis 1898–1963 46.1% The Lion, the Witch and the Wardrobe 10.1% 618
Neil Gaiman 1960– 45.9% American Gods 9.1% 575
Stephenie Meyer 1973– 28.2% Twilight 26.1% 72
Dan Brown 1964– 23.8% The Da Vinci Code 38.3% 35
Dean Koontz 1945– 22.7% Odd Thomas 4.2% 342
Mercedes Lackey 1950– 21.3% Arrows of the Queen 2.3% 295
George R. R. Martin 1948– 21.0% A Game of Thrones 21.4% 494

Fantasy sells a lot more than SF!  Six authors here are bigger than Asimov, including the youngster Neil Gaiman.  The youngest author in both these lists is Stephenie Meyer, followed by Rowling.

Are you dismayed that fantasy and SF seem to dominate people’s collections?   Don’t worry – classic authors do very well too:

Author Lived % of Rowling’s copies Book with Most Copies % of author’s total Number works
William Shakespeare 1564–1616 40.8% The Complete Works of William Shakespeare 9.0% 4336
Agatha Christie 1890–1976 36.8% And Then There Were None 5.2% 1502
Jane Austen 1775–1817 30.6% Pride and Prejudice 29.8% 705
Charles Dickens 1812–1870 29.3% Great Expectations 14.2% 1841
Mark Twain 1835–1910 19.2% Adventures of Huckleberry Finn 24.2% 2040
Ernest Hemingway 1899–1961 17.4% The Old Man and The Sea 19.3% 501
Fyodor Dostoevsky 1821–1881 16.6% Crime and Punishment 29.8% 952
Gabriel Garcia Marquez 1927–2014 15.0% One Hundred Years of Solitude 35.1% 289
Arthur Conan Doyle 1859–1930 14.4% The Hound of the Baskervilles 10.0% 2350
F. Scott Fitzgerald 1896–1940 14.1% The Great Gatsby 58.0% 425

Big Bill is way up there, and blows away those lightweights with 4336 works.  Even Dostoevsky and Marquez do well by this measure.

Is this a fair measure overall?  It’s certainly not a measure of overall influence – Austen and Dickens are without question important authors than Rowling or King.   It’s probably not a good measure of actual unit sales either, but that only matters to investors in publishing houses.  Maybe it’s best thought of as a sense of what people who care about books have actually read.    You’ve probably heard of all of these authors.   If not, give them a try!

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“2001” Was Completely Wrong

This year marks the 50th anniversary of the best SF movie ever made, 2001: A Space Odyssey.  I actually saw it when it first came out, and have seen it many times since then.  I think I’ve also read everything by Clarke and seen everything of Kubrick’s.

Yet what strikes me these days is how far off the movie is on, well, everything:

  • The prime use of intelligence is not murder.  The opening scene has the monolith uplifting a hairy hominid, who promptly starts using tools to kill his enemies.  Yet the distinctive characteristic of homo sapiens is not violence, but cooperation.  We live in vast social groups, and achieve enormous wealth because of trade.  Chimpanzees are actually much more violent than people.   Note that the famous jump cut from the flying bone to the flying orbital nuclear weapon was already wrong in 1968:
Watch: 4 Things All Great Edits Have in Common

Nope

The Outer Space Treaty had already banned nukes in spaces in 1967. It was easily passed because having nukes outside of one’s immediate control is a really terrible idea.   Having a dark view of human history is not rare, of course, and this movie was made not long after the worst war ever, but it’s still not right.

  • None of the space tech happened, and none of it will for the foreseeable future.  There was an orbital space plane, the Shuttle, but it was a disaster from the start.   Rotating a space station for gravity means that far more mass is needed for structural support, at enormous expense, and you’ll have pieces flying off. Moon bases aren’t in the cards because there’s nothing to do up there.   Nuclear rockets have all been cancelled because of safety issues.  Manned space flight in general is fading – the last space tourist was nine years ago, and many fewer individuals are flying now.  (see The Human Population of Space).
  • We’re not close to HAL’s general artificial intelligence.   More and more specific human abilities are now able to be done by machine, from image and speech recognition to language translation, but those are isolated programs.  Machines don’t make their own way in the world.   They don’t have their own will for just the reason shown in the movie – they’ll then do what we do NOT want.  AI programs are expensive industrial software, not children.   They better damn well do the right thing or else their programmers will all be fired.

Why does all this matter?   Because 2001 was as good as it gets for SF.  It hit most of the field’s tropes – aliens, space, robots – and did it as well as anyone could do in 1968.  No sound in space, no dogfights in vacuum, no whizzing past nebulae.  It took on big themes like technology and evolution, and what transcendence looks like.   It still has that core feeling of SF, of alienation and wonder, but its future just never happened.

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Mad Science #3: Land Mine Follies

Two stories of mad science this time about this vicious class of weapons, and one about how they ought to be done:

Radioactive Nazi Land Mines

Like Mad Science #1, the first story comes from Atomic Adventures (2017) by James Mahaffey.   Ordinary land mines have steel or aluminum cases, and so can be found by metal detectors.  These work by inducing a current in the object with a changing magnetic field, and then picking up the object’s field.  To defeat that, you can make the mine out of something non-conductive, and the Nazis actually built 11 million mines with glass shells.  These had the added ‘feature’ of riddling people with glass shards, which are hard to see on X-rays.

But you don’t just want to hide mines – you want  to be able to find them yourself.  So the Nazis came up with another idea – make them radioactive.   They can then be detected with Geiger counters.   In 1944/45 they built a class of anti-tank mines called Topfmines which were painted with a material called ‘tarnsand’.   No one seems to know quite what this was, but it appears to be tailings from uranium mining.   The mine’s case was made of pressed wood pulp, and it contained 6 kg of TNT.  It had a pressure plate on the top and a trigger that responded to 150 kg of pressure.  That’s heavier than a person (at least in those days), but would be set off by a vehicle.

https://i0.wp.com/www.lonesentry.com/ordnance/wp-content/uploads/2010/01/topf-mine.jpg

Topfmine Radioactive Anti-tank Mine – Click for description.  Bottom right image shows the bottom of the mine and its carrying handle

They then mounted a Geiger counter called the Stuttgart 43 on a long pole and attached it to the front of tanks.   It could pick up this mine long before they drove over it.

The Allies never caught onto this.   About 800,000 were made in 1944 and 45.   They were probably laid in France and Poland to stop Allied advances, and many may still be there, along with so much other unexploded ordnance.  The casings would degrade over time, and the charges would also deteriorate, but the radioactivity would last forever.  They’re just another memento of Nazi occupation.

British Nuclear Land Mines, Heated by Chickens

One expects craziness from Nazis, but an even madder project came from the British.   They started developing their own nuclear weapons in the 1950s after the US cut off research cooperation due to spying scandals.  Their first bomb was called Blue Danube, and went into production in 1956.   This was a huge implosion device, weighing about 5 tons, with about a 10 kiloton yield.   That’s a hard thing to move by bomber, so they thought about other applications for the same design.   They hit upon using it as a land mine on the plains of Northern Germany.   If the Cold War turned hot, and thousands of Soviet tanks rolled out from East Germany to attack the West, these would be set off by timers or miles-long wires for remote detonators.   The project was called Blue Peacock and two were actually built:

That Time the British Developed a Chicken Heated Nuclear Bomb

Blue Peacock Nuclear Land Mine in the collection of the UK Atomic Weapons Establishment (AWE, awe.co.uk).  Click for AWE article by curator

Yes, turning Germany into a radioactive wasteland just to block tanks was a deeply terrible idea.  But, they reasoned, it would be even worse if it didn’t work.   These bombs were just sitting there in the cold ground.  How could they be sure that the timers and detonators wouldn’t freeze up in the winter?  They considered swathing them in glass fiber pillows, but then hit on a much better idea – put a crate of chickens inside.  Their body heat would amount to about 10 watts per chicken.  Keep them from pecking at the wiring, give them some feed and water, and they would be fine, at least until they were vaporized.

This was discovered on April 1st, 2004, when the program was declassified after 50 years.  April 1st, eh?   But no, it wasn’t a prank – there were archival drawings of just where the coop would go.  Wasn’t that rather cruel to the chickens?   Well, when setting off an atomic bomb, the health of chickens is low on one’s priority list.

Although ten of them were proposed to be built, the whole program was cancelled in 1958 when they came to their senses.  However, the US did go on to build nuclear land mines, the Medium Atomic Demolition Munition, and deployed them between 1961 and 1989 in Europe, South Korea, and possibly even the Golan Heights.

Modern Mine Replacements

Land mines are horrible anyway, and injure many thousands of people a year, often children playing in abandoned fields.   Most countries are banning them under the auspices of the Ottawa Land Mine Treaty.  Unfortunately, the major military powers – the US, Russia, China, and India – have refused to sign.  In spite of spending trillions on their militaries, they still like this cheap and dangerous weapon, even though it injures their own people.

But if there have to be minefields, let’s at least make them safer.  A friend of mine suggested that instead of strewing a field with explosives, strew it with sensors.  When they detect a person or vehicle crossing a restricted area, signal an automated mortar.  It drops a shell on the detected position within a couple of seconds.   The signals are encrypted to prevent spoofing, and the sensors disable themselves if disturbed.  The whole thing can be disabled if your own troops are entering the area, and shut down when the front changes position.  This is just what DARPA was trying to do with its Smart Dust program in the late 1990s.

Given the progress in Internet-of-Things electronics, this could well be cheaper than minefields!  These sensors could cost pennies.   Maybe then this weapon class can be eliminated everywhere.

 

 

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Is STEM Recruitment Working?

The technical world, that of math, science, and engineering, has been trying for decades to get more young people interested in it.   It collectively sponsors TV programs, high school contests, and scholarships.   Politicians are constantly touting the benefits of STEM careers, as are companies.

So has all this encouragement had an effect?   To check, let’s look to see if more people are entering the fields, as defined by getting bachelor’s degrees in them.  This should be a better guide than graduate degrees, because those are often not economic, and are heavily affected by how many foreign students come.  Let’s also derate by the number of people in the age group, to make sure it’s not some population shift.  The Census tracks population in five-year groupings, so let’s pick ages 20-24, which covers the usual age for  for when people get bachelor’s degrees. The number of people in that range has varied from 16M in 1969, up to 22M at the peak of the Boomers in 1983, down to 18M in 1997, and back up to 23M in 2015.

The National Center for Educational Statistics, a division of the NSF, tracks the number of degrees here: WebCASPAR database.  I’ve massaged all the data into this spreadsheet –  STEM Recruitment As Measured by Bachelor Degrees – but let me put the charts here with some description. So, first, engineering:

I’m including Computer Science under engineering, because science is the study of nature, not machinery.   CS is much the most popular degree, but interest in it varies a lot.  It peaked in 2003, when people got into it during the Dot-Com Bubble in the late 90s, crashed in the Great Recession, and is still not back to peak levels.

Mech E was stable for decades, but recently is on the rise, probably because of robotics. The TV shows Mythbusters and Junkyard Wars may also be helpful, since those stress mechanical invention above all other kinds of engineering.

EE peaked in the 80s, and has been on a long, slow decline since, although there’s a recent small up-tick.  EE is a capital-intensive field these days, unlike most of its history, and so recruitment is down.

Civil is pretty constant, as are Industrial and Aerospace, but Chemical is doing well.

Other is a catch-all for many categories, and is doing very well.   Its major categories are Biochemical, Biomedical, Mechatronic, Naval and Ocean Engineering, Nuclear, and Systems.  The data doesn’t break this down, but I would expect that the bio-oriented and the robot-oriented ones have big increases.

Now let’s look at math and the major sciences:

Biology utterly rules, and is doing great.  About twice as many people get bachelors in biology as in CS.  In fact, there are more biologists than all engineering fields combined.  This is partly because Bio is an entry degree for medicine, and partly because Bio really is the dominant field of scientific research these days.

Math is actually down from its level in the 1960s, but is on a slow rise these days.   CS probably took away the more practically-oriented math people in the 1970s.

Chemistry, physics, and the natural sciences (Astronomy, Meteorology, Oceanography, and Geology) are all stagnant.

The above are the so-called hard sciences, a term I dislike, but they’re the ones that concern the natural world.  The ones that concern the human world are more popular:

Psychology and Sociology are just fundamentally more interesting to us humans than fields that deal with abstract forces or invisible molecules.  I think we’re on a threshold in these fields of being able to truly model what’s happening in them, which should lead to breakthroughs at least as big as those of 19th century physics and 20th century chemistry.   Like those, they can also be used for ill, as I mentioned in Weaponized Psychology Helped Elect Trump  and in When Modeling Goes Bad – “Weapons of Math Destruction” .  But understanding is always key to progress, and these fields are moving fast.

Medical Sciences is on an upswing as part of medicine in general, but Anthropology seems constant, perhaps because too much of the world is inter-connected.  Linguistics as actually on a good upswing but can’t be seen at this scale.

Finally, let’s look at how STEM fields compare to the trends in degrees as a whole:

The large fields that are growing are Business (unsurprising as the country becomes more mercantile), Natural Science (largely Biology), and Human Science (largely Psychology).  Engineering is on a slight rise (largely CS), and Humanities and Education are flat.  The big changes happened in the 1980s, when Humanities and Education were displaced by Business, probably as opportunities for women grew.

So what can we say overall?   It doesn’t really look that good for STEM.   Biology and CS are up, but they’re volatile.   Other STEM fields are largely flat or only slowly growing.  My own field, EE, is actually declining.  STEM promoters are almost certainly not trying to increase the number of Psychology majors, but that’s doing very well.   Maybe this promotion has a minor effect compared to people’s inherent interest in fields and the career prospects for it.

 

 

 

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