Hackaday

If you’re looking for a long journey into the wonderful world of instrument hacking, [Arty Farty Guitars] is six parts into a seven part series on hacking an existing guitar into a guitar-hurdy-gurdy-hybrid, and it is “a trip” as the youths once said. The first video is embedded below.

The Hurdy-Gurdy is a wheeled instrument from medieval europe, which you may have heard of, given the existence of the laser-cut nerdy-gurdy, the electronic midi-gurdy we covered here, and the digi-gurdy which seems to be a hybrid of the two. In case you haven’t seen one before, the general format is for a hurdy-gurdy is this : a wheel rubs against the strings, causing them to vibrate via sliding friction, providing a sound not entirely unlike an upset violin. A keyboard on the neck of the instrument provides both fretting and press the strings onto the wheel to create sound. 

[Arty Farty Guitars] is a guitar guy, so he didn’t like the part with about the keyboard. He wanted to have a Hurdy Gurdy with a guitar fretboard. It turns out that that is a lot easier said than done, even when starting with an existing guitar instead of from scratch, and [Arty Farty Guitar] takes us through all of the challenges, failures and injuries incurred along the way. 

Probably the most interesting piece of the puzzle is the the cranking/keying assembly that allows one hand to control cranking the wheel AND act as keyboard for pressing strings into the wheel. It’s key to the whole build, as combining those functions on the lower hand leaves the other hand free to use the guitar fretboard half of the instrument. That controller gets its day in video five of the series. It might inspire some to start thinking about chorded computer inputs– scrolling and typing?

If you watch up to the sixth video, you learn that that the guitar’s fretting action is ultimately incompatible with pressing strings against the wheel at the precise, constant tension needed for good sound. To salvage the project he had to switch from a bowing action with a TPU-surfaced wheel to a sort of plectrum wheel, creating an instrument similar to the thousand-pick guitar we saw last year.

Even though [Arty Farty Guitars] isn’t sure this hybrid instrument can really be called a Hurdy Gurdy anymore, now that it isn’t using a bowing action, we can’t help but admire the hacking spirit that set him on this journey. We look forward to the promised concert in the upcoming 7th video, once he figures out how to play this thing nicely.

Know of any other hacked-together instruments that possibly should not exist? We’re always listening for tips.

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According to [Casey Connolly], Qualcomm’s release of how to interact with their embedded USB debugging (EUD) is a big deal. If you haven’t heard of it, nearly all Qualcomm SoCs made since 2018 have a built-in debugger that connects to the onboard USB port. The details vary by chip, but you write to some registers and start up the USB phy. This gives you an oddball USB interface that looks like a seven-port hub with a single device “EUD control interface.”

So what do you do with that? You send a few USB commands, and you’ll get a second device. This one connects to an SWD interface. Of course, we have plenty of tools to debug using SWD.

In particular, there’s a fork of OpenOCD that knows how to use EUD, although it required a library that wasn’t available to us mere mortals. But now it is, so smooth sailing, right?

Um, no. Unless you have a very specific build configuration, the code won’t compile. Luckily, the fixes are not that hard and are available. The OpenOCD fork is a bit out of date, too. But with perseverance, it all worked.

In addition to the SWD device, there appears to be a COM and trace peripheral available, although those may need more work to be usable. If you make progress on those, let us know.

SWD debugging can be very handy. While not everyone likes debuggers, we’ve been a fan of hardware-based debugging for a long time.

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When we do textbook analysis, we tend to ignore the real-world concerns for the sake of learning. So, a typical theoretical voltage divider is simply two resistors. But if you examine a low-pass RC filter, you’ll see a single resistor and a capacitor. What if you combine them? That’s what [Old Hack EE] did in a recent video, and you can check it out below.

It helps if you are familiar with Thevenin equivalents and, of course, Ohm’s Law. There’s also a bit of algebra, but nothing too complicated. The example design has a lossy filter at 100 Hz.

Of course, RC filters are easy to understand if you think of them as voltage dividers with a frequency-variable resistance, which is what the math is basically saying. The load impedance, in this case, is R2 in parallel with Xc at a given frequency.

He mentions that you might find a circuit like this in a power supply. However, it is also common to see this circuit wherever a divider drives a load with capacitance or even parasitic capacitance in cables or circuit boards.

We’ve discussed Thevenin equivalence modeling before. If you want really good filters, you are probably going to need op-amps.

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Immutable distributions are slowly spreading across the Linux world– but should you care? Are they hacker friendly? What does “immutable” mean, anyway?

Immutable means “not subject or susceptible to change” according to Merriam-Webster, which is not 100% accurate in this context, but it’s close enough and the name is there so we’re stuck with it. Immutable distributions are subject to change, it’s just that how you change them is quite a bit different than bog-standard Linux. Will this matter to you? Read on to find out! (Or, if you know the answers already, read on to find out how angry you should be in the comments section.)

Immutability is cloud-based thinking: the system has a known-good state, and it’s always in it. Everything that is not part of the core system is containerized and controlled. I’m writing this from a KDE-based distribution called Aurora, part of the Universal Blue project that builds on Fedora’s Atomic Desktop work. It bills itself as being for “lazy developers”.

The advantage to this hypothetical lazy dev is that the base system is already built, and you can’t get distracted messing around with it. It works, and it isn’t at all likely to break. Every installation is essentially identical to every other installation, which means reproducibility is all but guaranteed. No more faffing about arguing on forums to figure out which library is conflicting with which. In an immutable system, they’ve all been selected to play well together, and anything else is safely containerized. (Again, a cloud ideal.) If the devs make a mistake during an update, well, just roll back! 

50 Shades of Immunability

The different flavours of immutable linux differ in how they accomplish that, but all have rollbacks as a basic capability. Each change to the system becomes a new, indivisible image; that’s why we talk about atomic updates. You create a new system image when you update, but you don’t start using it until you reboot the system. (This has some advantages to stability, as you might imagine, although the rebooting can get old.) The old image is maintained on your system, just in case you happen to need it.

MicroOS and its descendants (like Aeon) use a system based on BRTFS snapshots to provide rollbacks. Fedora’s atomic desktops, like Silverblue, and the Universal Blue downstreams that are based on Fedora like Bazzite or Aurora use a system called OSTree, which is considerably more complex and more interesting. You can do something similar with Nix, of course, but that is a whole other kettle of fish.

OSTree bills itself as “Git for operating system binaries”. Every update, or every package installed is layered onto the tree and can be rolled back if needed– en masse, or individually. You can package up that tree of commits, and deploy it onto a new system, making devising new “distros” so trivial they don’t really deserve the name. In theory, you can install everything via OSTree, but the further you take your system from the base image, the less you have that “every system is identical” easy-problem-solving that the immutable guys like to talk about. 

Of course you do want to install applications, and you do it the same way you might on a server: in containers. What sort of containers can vary by taste, but typically that means Flatpak for GUI applications. Fedora-based immutable distributions like Silverblue or Aurora use Flatpak, as does OpenSuse. (AppImage and snap are also options, technically speaking, but who likes snaps?) The Universal Blue team adds in Homebrew for those terminal applications that don’t tend to get Flatpaks. I admit that I was surprised at first to see Homebrew when I started using Aurora, since I knew it as “the missing package manager for MacOS” but its inclusion makes perfect sense when you think about it. 

MacOS is the First Immutable UNIX

MacOS, you see, is the first immutable UNIX. As much as we in the Linux community don’t like to talk about it, Macs aren’t just POSIX compatible– they run Certified UNIX(). And Curputino has been moving towards this “immutable” thing for a long time, until Catalina finally sealed the system folders away completely on a read-only volume. Updates for MacOS also come as snapshots to replace that system volume– you could certainly call them “atomic”. Since the system volume is locked down, traditional package managers won’t be able operate. Homebrew was created to solve that problem. It works just as well on a Linux system that has the same lockdown applied to its system folders.

If Homebrew isn’t your cup of tea – and it seems to not be everyone’s, since I think Universal Blue is the only distro set to ship with it – you can go more hard-core into containerization with docker or podman. Somewhere in between, you could use something like Distrobox. If you haven’t heard of it, Distrobox is a framework for deploying traditional linux systems inside containers. For devs, it’s great for testing, even if you aren’t basing it on top of an immutable distribution. If you’ve never worked in the cloud, this may all sound like rube-goldberg gobbbly-removed, (“linux in a box on my linux!?”) but once you adapt to it, it’s not so bad.

The Year of Immutable on the Desktop?

The question is: do you want to adapt to it? Is cloud-based thinking necessary on the desktop? Well I’d say it depends on who is using the desktop. I would absolutely steer Windows users who are thinking of switching to Linux in the wake of the Windows 10 EOL to a Universal Blue distribution, and probably Aurora since KDE is more windows-y than Gnome. Most of those ex-Windows users are people who just want to use a computer, not play with it. If that describes you, then maybe an immutable distribution could be to your liking.

MacOS has shown that very few desktop users will ever notice if they can access the system folders or not; they are most interested in having a stable, reproducible environment to work in. Thus, immutable Linux may be the way to bring Linux mainstream – certainly Steam thinks so, with SteamOS. For their use case, it’s hard to argue the benefits: you need a stable base system for the stack of cards that is gaming on Linux, and tech support is much simplified for a locked-down operating system that you cannot install packages on. The rising popularity of Bazzite, Universal Blue’s gaming-centric distribution, also speaks to this.

There are downsides to this kind of system, of course, and it is important to recognize that. Some people really, really hate containerization because Flatpaks, and other similar options, use more memory, both on disk and in RAM. Of course not everything is available as a Flatpak, or on Homebrew if the system uses that. If you want to use Toolbox or Distrobox to get a distro-specific set of packages, well, of course running a whole extra Linux system in a container is going to have overhead. 

From an aesthetic perspective, it’s not as elegant as a traditional Linux environment, at least to some eyes, mine included. Those of us who switched to Linux because we wanted absolute control over our computers might not feel too great about the “do not touch” label implicitly scrawled across the system folders, even if we do get something like rpm-ostree to make changes with. Even with a package manager, there are customizations and tweaks you simply cannot make on a read-only system.  For those of us who treat Linux as a hobby, that’s probably a no-go. 

For the “Lazy Developer” Aurora sells itself to, well, that’s perhaps a different story. Speaking of lazy, I’ve been using Aurora for a few months now, almost in spite of myself. I initially loaded it as the last step on a distro-hopping jaunt to see if I could find a good Windows 10 replacement for my parents. (I think this is it, to be honest.) It’s still on my main laptop simply because it’s so unobtrusively out of the way that I can think of no reason to install anything else.

At some point that may change, and when it does I might just overcorrect and do a Linux From Scratch build or try out like NixOS like I’ve been meaning to. Something like that would let me regain the sense of agency I have forfeited to the Universal Blue dev team while running Aurora. (There have been times where I can feel the ghostly hand of an imaginary sysadmin urging me not to mess with my own system.) 

After seeing how well containerization can work on desktop, Nix looks extra appealing – it can do most of what this article talks about with the immutable distros, but without trusting configuration of any facet of the system to anyone else. What do you think? Are the touted benefits to stability, reproducibility, and security worth the hassle of an immutable distribution? Is the grass greener in the land of Nix? If you’ve tried one of the immutable Linux distributions out there, we’d love to hear what you think in the comments.

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While we know that many of you are reading Hackaday via our Really Simple Syndication (RSS) feed, we suspect that most people on the street wouldn’t know that it underlies a lot of the modern internet. [A. McNamee] and [A. Service] have created an illustrated history of RSS that proudly proclaims RSS is (not) dead (yet)!

While tens of millions of users used Google Reader before it was shut down, social media and search companies have tried to squeeze independent blogs and websites for an increasingly large part of their revenue, making it more and more difficult to exist outside the walled gardens of Facebook, Apple, Google, etc. Despite those of you that remember, RSS has been mostly forgotten.

RSS has been the backbone of the podcast industry, however, quietly serving feeds to millions of users everywhere with few of them aware that an open protocol from the 90s was serving up their content. As with every other corner of the internet where money could be made, corporate raiders have come to scoop up creators and skim the profits for themselves. Spotify has been the most egregious actor here, but the usual suspects of Apple, Google, and Amazon are also making plays to enclose the podcast commons.

If you’d like to learn more about how big tech is sucking the life out of the internet (and possibly how to reverse the enshittification) check out Cory Doctorow’s keynote from our very own Supercon.

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We love 3D printing. We’ll print brackets, brackets for brackets, and brackets to hold other brackets in place. Perhaps even a guilty-pleasure Benchy. But 3D printed shoes? That’s where we start to have questions.

Every few months, someone announces a new line of 3D-printed footwear. Do you really want your next pair of sneakers to come out of a nozzle? Most of the shoes are either limited editions or fail to become very popular.

First World Problem

You might be thinking, “Really? Is this a problem that 3D printing is uniquely situated to solve?” You might assume that this is just some funny designs on some of the 3D model download sites. But no. Adidas, Nike, and Puma have shoes that are at least partially 3D printed. We have to ask why.

We are pretty happy with our shoes just the way that they are. But we will admit, if you insist on getting a perfect fitting shoe, maybe having a scan of your foot and a custom or semi-custom shoe printed is a good idea. Zellerfield lets you scan your feet with your phone, for example. [Stefan] at CNC Kitchen had a look at those in a recent video. The company is also in many partnerships, so when you hear that Hugo Boss, Mallet London, and Sean Watherspoon have a 3D-printed shoe, it might actually be their design from Zellerfield.

Or, try a Vivobiome sandal. We aren’t sold on the idea that we can’t buy shoes off the rack, but custom fits might make a little sense. We aren’t sure about 3D-printed bras, though.

Maybe the appeal of 3D-printed shoes lies in their personalizability? Creating self-printed shoes might make sense, so you can change their appearance or otherwise customize them. Maybe you’d experiment with different materials, colors, or subtle changes in designs. Nothing like 30 hours of printing and three filament changes to make one shoe. And that doesn’t explain why the majors are doing it.

Think of the Environment!

There is one possible plus to printing shoes. According to industry sources, more than 20 billion pairs of shoes are made every year, and almost all will end up in landfills. Up to 20% of these shoes will go straight to the dump without being worn even once.

So maybe you could argue that making shoes on demand would help reduce waste. We know of some shoe companies that offer you a discount if you send in an old pair for recycling, although we don’t know if they use them to make new shoes or not. Your tolerance for how much you are willing to pay might correlate to how much of a problem you think trash shoes really are.

But mass-market 3D-printed shoes? What’s the appeal? If you’re desperate for status, consider grabbing a pair of 3D-printed Gucci shoes for around $1,300. But for most of us, are you planning on dropping a few bucks on a pair of 3D-printed shoes? Why or why not? Let us know in the comments.

If you are imagining the big guys printing shoes on an Ender 3, that’s probably not the case. The shoes we’ve seen are made on big commercial printers.

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If you’re into Macs, you’ll always remember your first. Maybe it was the revolutionary classic of 1984 fame, perhaps it was the adorable G3 iMac in 1998, or even a shiny OS X machine in the 21st century. Whichever it is, you’ll find it emulated in [Marcin Wichary]’s essay “Frame of preference: A history of Mac settings, 1984–2004” — an exploration of the control panel and its history.

That’s not a photograph, it’s an emulator. (At least on the page. Here, it’s a screenshot.)

[Marcin] is a UI designer as well as an engineer and tech historian, and his UI chops come out in full force, commenting and critiquing Curputino’s coercions. The writing is excellent, as you’d expect from the man who wrote the book on keyboards, and it provides a fascinating look at the world of retrocomputing through the eyes of a designer. That design-focused outlook is very apropos for Apple in particular. (And NeXT, of course, because you can’t tell the story of Apple without it.)

There are ten emulators on the page, provided by [Mihai Parparita] of Infinite Mac. It’s like a virtual museum with a particularly knowledgeable tour guide — and it’s a blast, getting to feel hands-on, the design changes being discussed. There’s a certain amount of gamification, with each system having suggested tasks and a completion score when you finish reading. There are even Easter eggs.

This is everything we wish the modern web was like: the passionate deep-dives of personal sites on the Old Web, but enhanced and enabled by modern technology. If you’re missing those vintage Mac days and don’t want to explore them in browser, you can 3D print your own full-size replica, or a doll-sized picoMac.

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The aerospike engine holds great promise for spaceflight, but for various reasons, has remained slightly out of reach for decades. But thanks to Leap 71, the technology has moved one step closer to a spacecraft near you with the test fire of their generatively-designed, 3D printed aerospike.

We reported on the original design process of the engine, but at the time it hadn’t been given a chance to burn its liquid oxygen and kerosene fuel. The special sauce was the application of a computational physics model to tackle the complex issue of keeping the engine components cool enough to function while directing 3,500˚C exhaust around the eponymous spike.

Printed via a powder bed process out of CuCrZr, cleaned, heat treated, and then prepped by the University of Sheffield’s Race 2 Space Team, the rocket produced 5,000 Newtons (1,100 lbf) of thrust during its test fire. For comparison, VentureStar, the ill-fated aerospike single stage to orbit project from the 1990s, was projected to produce more than 1,917 kilonewtons (431,000 lbf) from each of its seven RS-2200 engines. Leap 71 obviously has some scaling up to do before this can propel any crewed spacecraft.

If you want to build your own aerospike or 3D printed rocket nozzles we encourage you to read, understand, and follow all relevant safety guidelines when handling your rockets. It is rocket science, after all!

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[Ryan] purchased a large fume extractor designed to sit on the floor below the work area and pull solder fumes down into its filtering elements. The only drawback to this new filter was that its controls were located near his feet. Rather than kicking at his new equipment, he devised a way to automate it.

By adding a Wemos D1 Mini microcontroller running ESPHome, a relay board, and a small AC-to-DC transformer, [Ryan] can now control the single push button used to cycle through speed settings wirelessly. Including the small transformer inside was a clever touch, as it allows the unit to require only a single power cable while keeping all the newfound smarts hidden inside.

The relay controls the button in parallel, so the physical button still works. Now that the extractor is integrated with Home Assistant, he can automate it. The fan can be controlled via his phone, but even better, he automated it to turn on by monitoring the power draw on the smart outlet his soldering iron is plugged into. When he turns on his iron, the fume extractor automatically kicks in.

Check out some other great automations we’ve featured that take over mundane tasks.

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The volume slider on our virtual desktops is a skeuomorphic callback to the volume sliders on professional audio equipment on actual, physical desktops. [Maker Vibe] decided that this skeuomorphism was so last century, and made himself a physical audio control box for his PC.

Since he has three audio outputs he needs to consider, the peripheral he creates could conceivably be called a fader. It certainly has that look, anyway: each output is controlled by a volume slider — connected to a linear potentiometer — and a mute button. Seeing a linear potentiometer used for volume control threw us for a second, until we remembered this was for the computer’s volume control, not an actual volume control circuit. The computer’s volume slider already does the logarithmic conversion. A Seeed Studio Xiao ESP32S3 lives at the heart of this thing, emulating a Bluetooth gamepad using a library by LemmingDev. A trio of LEDs round out the electronics to provide an indicator for which audio channels are muted or active.

Those Bluetooth signals are interpreted by a Python script feeding a software called Voicmeeter Banana, because [Maker Vibe] uses Windows, and Redmond’s finest operating system doesn’t expose audio controls in an easily-accessible way. Voicmeeter Banana (and its attendant Python script) takes care of telling Windows what to do. 

The whole setup lives on [Maker Vibe]’s desk in a handsome 3D printed box. He used a Circuit vinyl cutter to cut out masks so he could airbrush different colours onto the print after sanding down the layer lines. That’s another one for the archive of how to make front panels.

If volume sliders aren’t doing it for you, perhaps you’d prefer to control your audio with a conductor’s baton.

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[Cal Bryant] hacked together a home automation system years ago, which more recently utilizes Piper TTS (text-to-speech) voices for various undisclosed purposes. Not satisfied with the robotic-sounding standard voices available, [Cal] set about an experiment to fine-tune the Piper TTS AI voice model using a clone of a single phrase created by a commercial TTS voice as a starting point.

Before the release of Piper TTS in 2023, existing free-to-use TTS systems such as espeak and Festival sounded robotic and flat. Piper delivered much more natural-sounding output, without requiring massive resources to run. To change the voice style, the Piper AI model can be either retrained from scratch or fine-tuned with less effort. In the latter case, the problem to be solved first was how to generate the necessary volume of training phrases to run the fine-tuning of Piper’s AI model. This was solved using a heavyweight AI model, ChatterBox, which is capable of so-called zero-shot training. Check out the Chatterbox demo here.

As the loss function gets smaller, the model’s accuracy gets better

Training began with a corpus of test phrases in text format to ensure decent coverage of everyday English. [Cal] used ChatterBox to clone audio from a single test phrase generated by a ‘mystery TTS system’ and created 1,300 test phrases from this new voice. This audio set served as training data to fine-tune the Piper AI model on the lashed-up GPU rig.

To verify accuracy, [Cal] used OpenAI’s Whisper software to transcribe the audio back to text, in order to compare with the original text corpus. To overcome issues with punctuation and differences between US and UK English, the text was converted into phonemes using espeak-ng, resulting in a 98% phrase matching accuracy.

After down-sampling the training set using SoX, it was ready for the Piper TTS training system. Despite all the preparation, running the software felt anticlimactic. A few inconsistencies in the dataset necessitated the removal of some data points. After five days of training parked outside in the shade due to concerns about heat, TensorBoard indicated that the model’s loss function was converging. That’s AI-speak for: the model was tuned and ready for action! We think it sounds pretty slick.

If all this new-fangled AI speech synthesis is too complex and, well, a bit creepy for you, may we offer a more 1980s solution to making stuff talk? Finally, most people take the ability to speak for granted, until they can no longer do so. Here’s a team using cutting-edge AI to give people back that ability.

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We always enjoy [FloatHeadPhysics] explaining any math or physics topic. We don’t know if he’s acting or not, but he seems genuinely excited about every topic he covers, and it is infectious. He also has entertaining imaginary conversations with people like Feynman and Einstein. His recent video on tensors begins by showing the vector form of Ohm’s law, making it even more interesting. Check out the video below.

If you ever thought you could use fewer numbers for many tensor calculations, [FloatHeadPhysics] had the same idea. Luckily, imaginary Feynman explains why this isn’t right, and the answer shows the basic nature of why people use tensors.

The spoiler: vectors and even scalars are just a special case of tensors, so you use tensors all the time, you just don’t realize it. He works through other examples, including an orbital satellite and a hydroelectric dam.

We love videos that help us have aha moments about complex math or physics. It is easy to spew formulas, but there’s no substitute for having a “feeling” about how things work.

The last time we checked in with [FloatHeadPhysics], he convinced us we were already travelling at the speed of light. We’ve looked at a simple tensor explainer before, if you want a second approach.

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This week Jonathan chats with Joseph P. De Veaugh-Geiss about KDE’s eco initiative and the End of 10 campaign! Is Open Source really a win for environmentalism? How does the End of 10 campaign tie in? And what does Pewdiepie have to do with it? Watch to find out!

* End Of 10 campaign: https://endof10.org/* KDE Eco project: https://eco.kde.org/

Did you know you can watch the live recording of the show right on our YouTube Channel? Have someone you’d like us to interview? Let us know, or contact the guest and have them contact us! Take a look at the schedule here.

Direct Download in DRM-free MP3.

If you’d rather read along, here’s the transcript for this week’s episode.

Places to follow the FLOSS Weekly Podcast:

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Theme music: “Newer Wave” Kevin MacLeod (incompetech.com)

Licensed under Creative Commons: By Attribution 4.0 License

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It should probably come as no surprise to anyone that the images which we look at every day – whether printed or on a display – are simply illusions. That cat picture isn’t actually a cat, but rather a collection of dots that when looked at from far enough away tricks our brain into thinking that we are indeed looking at a two-dimensional cat and happily fills in the blanks. These dots can use the full CMYK color model for prints, RGB(A) for digital images or a limited color space including greyscale.

Perhaps more interesting is the use of dithering to further trick the mind into seeing things that aren’t truly there by adding noise. Simply put, dithering is the process of adding noise to reduce quantization error, which in images shows up as artefacts like color banding. Within the field of digital audio dithering is also used, for similar reasons. Part of the process of going from an analog signal to a digital one involves throwing away data that falls outside the sampling rate and quantization depth.

By adding dithering noise these quantization errors are smoothed out, with the final effect depending on the dithering algorithm used.

The Digital Era

Plot of a quantized signal and its error. (Source: Wikimedia)

For most of history, humanity’s methods of visual-auditory recording and reproduction were analog, starting with methods like drawing and painting. Until fairly recently reproducing music required you to assemble skilled artists, until the arrival of analog recording and playback technologies. Then suddenly, with the rise of computer technology in the second half of the 20th century we gained the ability to not only perform analog-to-digital conversion, but also store the resulting digital format in a way that promised near-perfect reproduction.

Digital optical discs and tapes found themselves competing with analog formats like the compact cassette and vinyl records. While video and photos remained analog for a long time in the form of VHS tapes and film, eventually these all gave way to the fully digital world of digital cameras, JPEGs, PNGs, DVDs and MPEG. Despite the theoretical pixel- and note-perfect reproduction of digital formats, considerations like sampling speed (Nyquist frequency) and the aforementioned quantization errors mean a range of new headaches to address.

That said, the first use of dithering was actually in the 19th century, when newspapers and other printed media were looking to print phots without the hassle of having a woodcut or engraving made. This led to the invention of halftone printing.

Polka Dots

Left: halftone dot pattern with increasing size downwards, Right: how the human eye would see this, when viewed from a sufficient distance. (Source: Wikimedia)

With early printing methods, illustrations were limited to an all-or-nothing approach with their ink coverage. This obviously meant serious limitations when it came to more detailed illustrations and photographs, until the  arrival of the halftone printing method. First patented in 1852  by William Fox Talbot, his approach used a special screen to break down an image into discrete points on a photographic plate. After developing this into a printing plate, these plates would then print this pattern of differently sized points.

Although the exact halftone printing methods were refined over the following decades, the basic principle remains the same to this day: by varying the size of the dot and the surrounding empty (white) space, the perceived brightness changes. When this method got extended to color prints with the CMYK color model, the resulting printing of these three colors as adjoining dots allowed for full-color photographs to be printed in newspapers and magazines despite having only so few ink colors available.

While it’s also possible to do CMYK printing with blending of the inks, as in e.g. inkjet printers, this comes with some disadvantages especially when printing on thin, low-quality paper, such as that used for newspapers, as the ink saturation can cause the paper to rip and distort. This makes CMYK and monochrome dithering still a popular technique for newspapers and similar low-fidelity applications.

Color Palettes

In an ideal world, every image would have an unlimited color depth. Unfortunately we sometimes have to adapt to a narrower color space, such as when converting to the Graphics Interchange Format (GIF), which is limited to 8 bits per pixel. This 1987-era and still very popular format thus provides an astounding 256 possible colors  -albeit from a full 24-bit color space – which poses a bit of a challenge when using a 24-bit PNG or similar format as the source. Simply reducing the bit depth causes horrible color banding, which means that we should use dithering to ease these sharp transitions, like the very common Floyd-Steinberg dithering algorithm:

From left to right: original image. Converted to web safe color. Web safe with Floyd-Steinberg dithering. (Source: Wikipedia)

The Floyd-Steinberg dithering algorithm was created in 1976 by Robert W. Floyd and Louis Steinberg. Its approach to dithering is based on error diffusion, meaning that it takes the quantization error that causes the sharp banding and distributes it across neighboring pixels. This way transitions are less abrupt, even if it means that there is noticeable image degradation (i.e. noise) compared to the original.

This algorithm is quite straightforward, working its way down the image one pixel at a time without affecting previously processed pixels. After obtaining the current pixel’s quantization error, this is distributed across the subsequent pixels following and below the current one, as in the below pseudo code:

for each y from top to bottom do for each x from left to right do oldpixel := pixels[x][y] newpixel := find_closest_palette_color(oldpixel) pixels[x][y] := newpixel quant_error := oldpixel - newpixel pixels[x + 1][y ] := pixels[x + 1][y ] + quant_error × 7 / 16 pixels[x - 1][y + 1] := pixels[x - 1][y + 1] + quant_error × 3 / 16 pixels[x ][y + 1] := pixels[x ][y + 1] + quant_error × 5 / 16 pixels[x + 1][y + 1] := pixels[x + 1][y + 1] + quant_error × 1 / 16

The implementation of the find_closest_palette_color() function is key here, with for a greyscale image a simple round(oldpixel / 255) sufficing, or trunc(oldpixel + 0.5) as suggested in this CS 559 course material from 2000 by the Universe of Wisconsin-Madison.

As basic as Floyd-Steinberg is, it’s still commonly used today due to the good results that it gives with fairly minimal effort. Which is not to say that there aren’t other dithering algorithms out there, with the Wikipedia entry on dithering helpfully pointing out a number of alternatives, both within the same error diffusion category as well as other categories like ordered dithering. In the case of ordered dithering there is a distinct crosshatch pattern that is both very recognizable and potentially off-putting.

Dithering is of course performed here to compensate for a lack of bit-depth, meaning that it will never look as good as the original image, but the less obnoxious the resulting artefacts are, the better.

Dithering With Audio

Although at first glance dithering with digital audio seems far removed from dithering the quantization error with images, the same principles apply here. When for example the original recording has to be downsampled to CD-quality (i.e. 16-bit) audio, we can either round or truncate the original samples to get the desired sample size, but we’d get distortion in either case. This distortion is highly noticeable by the human ear as the quantization errors create new frequencies and harmonics, this is quite noticeable in the 16- to 6-bit downsampling examples provided in the Wikipedia entry.

In the sample with dithering, there is clearly noise audible, but the original signal (a sine wave) now sounds pretty close to the original signal. This is done through the adding of random noise to each sample by randomly rounding up or down and counting on the average. Although random noise is clearly audible in the final result, it’s significantly better than the undithered version.

Random noise distribution is also possible with images, but more refined methods tend to give better results. For audio processing there are alternative noise distributions and noise shaping approaches.

Regardless of which dither method is being applied, it remains fascinating how the humble printing press and quantization errors have led to so many different ways to trick the human eye and ear into accepting lower fidelity content. As many of the technical limitations that existed during the time of their conception – such as expensive storage and low bandwidth – have now mostly vanished, it will be interesting to see how dithering usage evolves over the coming years and decades.

Featured image: “JJN Dithering” from [Tanner Helland]’s great dithering writeup.

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If you are a certain age, you probably remember the ads and publicity around Chisanbop — the supposed ancient art of Korean finger math. Was it Korean? Sort of. Was it faster than a calculator? Sort of. [Chris Staecker] offers a great look at Chisanbop, not just how to do it, but also how it became such a significant cultural phenomenon. Take a look at the video below. Long, but worth it.

Technically, the idea is fairly simple. Your right-hand thumb is worth 5, and each finger is worth 1. So to identify 8, you hold down your thumb and the first three digits. The left hand has the same arrangement, but everything is worth ten times the right hand, so the thumb is 50, and each digit is worth 10.

With a little work, it is easy to count and add using this method. Subtraction is just the reverse. As you might expect, multiplication is just repeated addition. But the real story here isn’t how to do Chisanbop. It is more the story of how a Korean immigrant’s system went viral decades before the advent of social media.

You can argue that this is a shortcut that hurts math understanding. Or, you could argue the reverse. However, the truth is that this was around the time the calculator became widely available. Math education would shift from focusing on getting the right answer to understanding the underlying concepts. In a world where adding ten 6-digit numbers is easy with a $5 device, being able to do it with your fingers isn’t necessarily a valuable skill.

If you enjoy unconventional math methods, you may appreciate peasant multiplication.

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Do you ever look at the news, and wonder about the process behind the news cycle? I did, and for the last couple of decades it’s been the subject of one of my projects. The Raspberry Pi on my shelf runs my word trend analysis tool for news content, and since my journey from curious geek to having my own large corpus analysis system has taken twenty years it’s worth a second look.

How Career Turmoil Led To A Two Decade Project

This is very much a minority spelling. Colin Smith, CC BY-SA 2.0.

In the middle of the 2000s I had come out of the dotcom crash mostly intact, and was working for a small web shop. When they went bust I was casting around as one does, and spent a while as a Google quality rater while I looked for a new permie job. These teams are employed by the search giant through temporary employment agencies, and in loose terms their job is to be the trained monkeys against whom the algorithm is tested. The algorithm chose X, and if the humans also chose X, the algorithm is probably getting it right. Being a quality rater is not in any way a high-profile job, but with the big shiny G on my CV I soon found myself in demand from web companies seeking some white-hat search engine marketing expertise. What I learned mirrored my lesson from a decade earlier in the CD-ROM business, that on the web as in any other electronic publishing medium, good content well presented has priority over any black-hat tricks.

But what makes good content? Forget an obsession with stuffing bogus keywords in the text, and instead talk about the right things, and do it authoritatively. What are the right things in this context? If you are covering a subject, you need to do so using the right language; that which the majority uses rather than language only you use. I can think of a bunch of examples which I probably shouldn’t talk about, but an example close to home for me comes in cider. In the UK, cider is a fermented alcoholic drink made from apples, and as a craft cidermaker of many years standing I have a good grasp of its vocabulary. The accepted spelling is “Cider”, but there’s an alternate spelling of “Cyder” used by some commercial producers of the drink. It doesn’t take long to realise that online, hardly anyone uses cyder with a Y, and thus pages concentrating on that word will do less well than those talking about cider.

We Brits rarely use the word “soccer” unless there’s a story about the Club World Cup in America.

I started to build software to analyse language around a given topic, with the aim of discerning the metaphorical cider from the cyder. It was a great surprise a few years later to discover that I had invented for myself the already-existing field of computational linguistics, something that would have saved me a lot of time had I known about it when I began. I was taking a corpus of text and computing the frequencies and collocates (words that appear alongside each other) of the words within it, and from that I could quickly see which wording mattered around a subject, and which didn’t. This led seamlessly to an interest in what the same process would look like for news data with a time axis added, so I created a version which harvested its corpus from RSS feeds. Thus began my decades-long project.

From Project Idea, To Corpus Appliance

In 2005 I knew how to create websites in the manner of the day, so I used the tools I had. PHP5, and MySQL. I know PHP is unfashionable these days, but at the time this wasn’t too controversial, and aside from all the questionable quality PHP code out there it remains a useful scripting language. Using MySQL however would cause me immense problems. I had done what seemed the right thing and created a structured database with linked tables, but I hadn’t fully appreciated just how huge was the task I had taken on. Harvesting the RSS firehose across multiple media outlets brings in thousands of stories every week, so queries which were near-instantaneous during my first development stages grew to take many minutes as my corpus expanded. It was time to come up with an alternative, and I found it in the most basic of OS features, the filesystem.

I have no idea why British news has more dog stories than cat stories.

Casting back to the 1990s, when you paid for web hosting it was given in terms of the storage space it came with. The processing power required to run your CGI scripts or later server-side interpreters such as ASP or PHP, wasn’t considered. It thus became normal practice to try to reduce storage use and not think about processing, and I had without thinking followed this path.

But by the 2000s the price of storage had dropped hugely while that of processing hadn’t. This was the decade in which cloud services such as AWS made an appearance, and as well as buying many-gigabyte hard disks for not a lot, you could also for the first time rent a cloud bucket for pennies. My corpus analysis system didn’t need to spend all its time computing if I could use a terabyte hard drive to make up for less processor usage, so I turned my system on its head. When collecting the RSS stories my retrieval script would pre-compute the final data and store it in a vast tree of tiny JSON files accessible at high speed through the filesystem, and then my analysis software could simply retrieve them and make its report. The system moved from a hard-working x86 laptop to a whisper-quiet and low powered Raspberry Pi with a USB hard disk, and there it has stayed in some form ever since.

Just What Can This Thing Do?

No prizes for guessing what happened this week.

So I have a news corpus that has taken me a long time to build. I can take one or more words, and I can compare their occurrence over time. I can watch the news cycle, I can see stories build up over time. I can even see trends which sometimes go against received opinion, such as spotting that the eventual winner of the 2016 UK Labour leadership race was likely to be Jeremy Corbyn early on while the herd were looking elsewhere. Sometimes as with the performance of the word “Brexit” over the middle of the last decade I can see the great events of our times in stark relief, but perhaps it’s in the non-obvious that there’s most value. If you follow a topic and it suddenly dries up for a couple of days, expect a really big story on day three, for example. I can also see which outlets cover one story more than another, something helpful when trying to ascertain if a topic is being pushed on behalf of a particular lobby.

My experiment in text analysis then turned into something much more, even dare I say it, something I find of help in figuring out what’s really going on in turbulent times. But from a tech point of view it’s taught me a huge amount, about statistics, about language, about text parsing, and even about watching the number of available inodes on a hard drive. Believe me, many millions of tiny files in a tree can become unwieldy. But perhaps most of all, after a lifetime of mucking about with all manner of projects but generating little of lasting significance, I can look at this one and say I created something useful. And that is something to be happy about.

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Normally you can’t read out the One Time Programming (OTP) memory in Microchip’s PIC MCUs that have code protection enabled, but an exploit has been found that gets around the copy protection in a range of PIC12, PIC14 and PIC16 MCUs.

This exploit is called PIC Burnout, and was developed by [Prehistoricman], with the cautious note that although this process is non-invasive, it does damage the memory contents. This means that you likely will only get one shot at dumping the OTP data before the memory is ‘burned out’.

The copy protection normally returns scrambled OTP data, with an example of PIC Burnout provided for the PIC16LC63A. After entering programming mode by setting the ICSP CLK pin high, excessively high programming voltage and duration is used repeatedly while checking that an area that normally reads as zero now reads back proper data. After this the OTP should be read out repeatedly to ensure that the scrambling has been circumvented.

The trick appears to be that while there’s over-voltage and similar protections on much of the Flash, this approach can still be used to affect the entire flash bit column. Suffice it to say that this method isn’t very kind to the fzslash memory cells and can take hours to get a good dump. Even after this you need to know the exact scrambling method used, which is fortunately often documented by Microchip datasheets.

Thanks to [DjBiohazard] for the tip.

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Some people slander retrocomputing as an old man’s game, just because most of those involved are more ancient than the hardware they’re playing with. But there are veritable children involved too — take the [ComputerSmith], who is recreating Conway’s game of life on a Macintosh Plus that could very well be as old as his parents. If there’s any nostalgia here, it’s at least a generation removed — thus proving for the haters that there’s more than a misplaced desire to relive one’s youth in exploring these ancient machines.

So what does a young person get out of programming on a 1980s Mac? Well, aside from internet clout, and possible YouTube monetization, there’s the sheer intellectual challenge of the thing. You cant go sniffing around StackExchange or LLMs for code to copy-paste when writing C for a 1986 machine, not if you’re going to be fully authentic. ANSI C only dates to 1987, after all, and figuring out the quirks and foibles of the specific C implementation is both half the fun, and not easily outsourced. Object Pascal would also have been an option (and quite likely more straightforward — at least the language was clearly-defined), but [ComputerSmith] seems to think the exercise will improve his chops with C, and he’s likely to be right. 

Apparently [ComputerSmith] brought this project to VCS Southwest, so anyone who was there doesn’t have to wait for Part 2 of the video to show up to see how this turns out, or to snag a copy of the code (which was apparently available on diskette). If you were there, let us know if you spotted the youngest Macintosh Plus programmer, and if you scored a disk from him.

If the idea of coding in this era tickles the dopamine receptors, check out this how-to for a prizewinning Amiga demo.  If you think pre-ANSI C isn’t retro enough, perhaps you’d prefer programming by card?

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Yes, you read that right– not benchy, but beanie, as in the hat. A toque, for those of us under the Maple Leaf. It’s not 3D printed, either, except perhaps by the loosest definition of the word: it is knit, by [Kevr102]’s motorized turbo knitter.

The turbo-knitter started life as an Addi Express King knitting machine. These circular knitting machines are typically crank-operated, functioning  with a cam that turns around to raise and lower special hooked needles that grab and knit the yarn. This particular example was not in good working order when [Kevr102] got a hold of it. Rather than a simple repair, they opted to improve on it.

A 12 volt motor with a printed gear and mount served for motorizing the machine. The original stitch counter proved a problem, so was replaced with an Arduino Nano and a hall effect sensor driving a 7-digit display. In theory, the Arduino could be interfaced with the motor controller and set to run the motor for a specific number of stitches, but in practice there’s no point as the machine needs babysat to maintain tension and avoid dropping stitches and the like. Especially, we imagine, when it runs fast enough to crank out a hat in under six minutes. Watch it go in the oddly cropped demo video embedded below.

Five minutes would still be a very respectable time for benchy, but it’s not going to get you on the SpeedBoatRace leaderboards against something like the minuteman we covered earlier.

If you prefer to take your time, this knitting machine clock might be more your fancy. We don’t see as many fiber arts hacks as perhaps we should here, so if you’re tangled up in anything interesting in that scene, please drop us a line.

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Many people who get analog electronics still struggle a bit to design oscillators. Even common simulators often need a trick to simulate some oscillating circuits. The Barkhausen criteria state that for stable oscillation, the loop gain must be one, and the phase shift around the feedback loop must be a multiple of 360 degrees. [All Electronics Channel] provides a thorough exploration of oscillators and, specifically, negative resistance, which is punctuated by practical measurements using a VNA. Check it out in the video below.

The video does have a little math and even mentions differential equations, but don’t worry. He points out that the universe solves the equation for you.

In an LC circuit, you can consider the losses in the circuit as a resistor. That makes sense. No component is perfect. But if you could provide a negative resistance, it would cancel out the parasitic resistance. With no loss, the inductor and capacitor will go back and forth, electrically, much like a pendulum.

So, how do you get a negative resistance? You’ll need an active device. He presents some example oscillator architectures and explains how they generate negative resistances.

Crystals are a great thing to look at with a VNA. That used to be a high-dollar piece of test gear, but not anymore.

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Bees are incredible insects that live and die for their hive, producing rich honey in complicated hive structures. The problem is as the average beekeeper, you wouldn’t see much of these intricate structures without disturbing the hive. So why not 3D print an observation hive? With [Teddy Hatcher]’s 3D printing creativity, that is exactly what he did.

Hexagonal sections allow for viewing of entire panels of hexagonal cells, growing new workers, and storing the rich syrup we all enjoy. Each module has two cell panels, giving depth to the hive for heat/humidity gradients. The rear of a module has a plywood backing and an acrylic front for ample viewing. [Teddy] uses three modules plus a Flow Hive for a single colony, enough room for more bees than we here at Hackaday would ever consider letting in the front door.

As with many 3D printed projects involving food or animals, the question remains about health down the line. Plastic can bio-accumulate in hives, which is a valid concern for anyone wanting to add the honey to their morning coffee. On the other hand, the printed plastic is not what honey is added to, nor what the actual cell panels are made from. When considering the collected honey, this is collected from the connected Flow Hive rather than anything directly in contact with 3D printed plastic.

Beehives might not always need a fancy 3D printed enclosure; the standard wooden crates seem to work just fine for most, but there’s a time and place for some bio-ingenuity. Conditions in a hive might vary creating problems for your honey production, so you better check out this monitoring system dedicated to just that!

Thanks to [George Graves] for the tip!

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If you need to cool something, the gold standard is using a gas compressor arrangement. Of course, there are definite downsides to that, like weight, power consumption, and vibrations. There are solid-state heat pumps — the kind you see in portable coolers, for example. But, they are not terribly efficient and have limited performance.

However, researchers at Johns Hopkins, working with Samsung, have developed a new thin-film thermoelectric heat pump, which they claim is easy to fabricate, scalable, and significantly more efficient. You can see a video about the new research below.

Manufacturing requires similar processes to solar cells, and the technology can make tiny heat pumps or — in theory — coolers that could provide air conditioning for large buildings. You can read the full paper in Nature.

CHESS stands for Controlled Hierarchically Engineered Superlattice Structures. These are nano-engineered thin-film superlattices (around 25 μm thick). The design optimizes their performance in this application.

The new devices claim to be 100% more efficient at room temperature than traditional devices. In practical devices, thermoelectric devices and the systems using them have improved by around 70% to 75%. The material can also harvest power from heat differences, such as body heat. The potential small size of devices made with this technology would make them practical for wearables.

We’ve looked at the traditional modules many times. They sometimes show up in cloud chambers.

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The Rigol oscilloscopes have a long history of modifications and hacks, and this latest from [Matthias] is an impressive addition; he’s been working on adding a function generator to the DHO800 line of scopes.

The DHO800 series offers many great features: it’s highly portable with a large 7-inch touchscreen, powered by USB-C, and includes plenty of other goodies. However, there’s room for enhancements. [Matthias] realized that while software mods exist to increase bandwidth or unlock logic analyzer functions, the hardware needed to implement the function generator—available in the more expensive DHO900 series—was missing.

To address this, he designed a daughterboard to serve as the function generator hardware, enabling features that software tweaks can unlock. His goal was to create an affordable, easy-to-produce, and easy-to-assemble interface board that fits in the space reserved for the official daughterboard in higher-end scopes.

Once the board is installed and the software is updated, the new functionality becomes available. [Matthias] clearly explains some limitations of his implementation. However, these shortcomings are outweighed by the tremendous value this mod provides. A 4-channel, 200 MHz oscilloscope with function generator capabilities for under $500 is a significant achievement. We love seeing these Rigol mods enhance tool functionality. Thanks, [Matthias], for sharing this project—great job bringing even more features to this popular scope.

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Humans have forever been using all manner of techniques to better secure the food we need to sustain our lives. The practice of agriculture is intimately tied to the development of society, while techniques like selective breeding and animal husbandry have seen our plants and livestock deliver greater and more nourishing bounty as the millennia have gone by. More recently, more direct tools of genetic engineering have risen to prominence, further allowing us to tinker with our crops to make them do more of what we want.

Recently, however, scientists have been pursuing a bold new technique. Researchers have explored using radiation from space to potentially create greater crops to feed more of us than ever.

“Cosmic Crops”

Most recently, an effort at “space mutagenesis” has been spearheaded by the International Atomic Energy Agency, a body which has been rather more notable for other works of late. In partnership with the UN’s Food and Agriculture Organization (FAO), it has been examining the effects that the space-based environment might have on seeds. Ideally, these effects would be positive, producing hardier crops with greater yields for the benefit of humanity.

The sorghum seeds that spent five months on the ISS as part of the joint FAO/IAEA research project. Credit: Katy Laffan/IAEA, CC BY 2.0

The concept is simple enough—put a bunch of seeds on the International Space Station (ISS), and see what happens. Specifically, researchers placed half the seeds outside the ISS, where they would be exposed to extreme cold and maximum doses of cosmic radiation. The other half were left inside the station as a control, where they would experience microgravity but otherwise be safe from temperature and radiation extremes. The hope was that the radiation may cause some random but beneficial mutations in the seed’s genetics which provide better crops for use on Earth.

Plant breeder and geneticist Anupama Hingane examines a sorghum plant grown at the FAO/IAEA Plant Breeding & Genetics Laboratory. Credit: Katy Laffan / IAEA, CC BY 2.0

Two types of seeds were sent up for the first trial by the IAEA and UN—sorghum, a nutrient-filled cereal grain, and arabidopsis, a fast-growing cress. After their flight on the ISS, they were returned to Earth to be germinated, grown, and examined for desirable traits. Of course, DNA sequencing was also on the table, to compare mutations generated in space with seeds kept inside the ISS and those irradiated under laboratory conditions.

The only thing missing from the IAEA’s experiment? A research paper. The seeds returned from space in April 2023, and were sent to the Plant Breeding and Genetics Laboratory in Seibersdorf, Austria soon after. We’ve seen pictures of the plants that sprouted from the seeds in space, but researchers are yet to publish full results or findings from the project.

Proven Benefits

It might sound like an oddball idea, particularly given the results from the IAEA’s project are yet to be delivered. However, space mutagenesis has been tried and tested to a greater degree than you might think. Chinese scientists have been experimenting with the technique of space mutagenesis for over 30 years, finding that it often delivers more beneficial mutations compared to using gamma rays in terrestrial labs.

Chinese efforts have seen many thousands of seeds irradiated via satellites and space stations, including a trip around the moon on the Chang’e-5 mission. Having been exposed to space radiation for anywhere from days to months, the seeds have returned to Earth and been planted and examined for beneficial mutations. While not every seed comes back better than before, some show rare mutations that offer breakthrough benefits in yield, drought resistance, fruit size, or temperature hardiness. These crops can then be bred further to refine the gains. Chinese efforts have experimented with everything from cotton to tomatoes, watermelons and corn, beyond others. A particular success story was Yujiao 1 – a sweet pepper variety released in 1990 boasting better fruit and resistance to disease, along with 16.4% higher yield than some comparable varieties.

A comparison of mutated peppers Yujiao 1 (Y1), Yujiao 2 (Y2), and Yujiao 3 (Y3) with comparable Longjiao wild types (marked W1,W2). Credit: research paper

The results of space mutagenesis are tracked very carefully, both by researchers involved and wider authorities. Notably, the IAEA maintains a Mutant Variety Database for plants that have been modified either by space-based radiation or a variety of other physical or chemical methods. This is important, and not only for reaping the benefits from mutagenic organisms. It’s also important to help researchers understand the mechanisms involved, and to help make sure that the risk of any negative traits breaking out into broader wild plant populations are mitigated.

Ultimately, space mutagenesis is just another tool in the toolbox for scientists looking to improve crops. It’s far from cheap to send seeds to space, let alone to do the research to weed out those with beneficial mutations from the rest. Still, the benefits on offer can be huge when scaled to the size of modern agriculture, so the work will go on regardless. It’s just another way to get more, something humans can never quite get enough of.

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Over the course of evolution microorganisms have evolved pathways to break down many materials. The challenge with the many materials that we humans have created over just the past decades is that we cannot wait for evolution to catch up, ergo we have to develop such pathways ourselves. One such example is demonstrated by [Nick W. Johnson] et al. with a recent study in Nature Chemistry that explicitly targets PET plastic, which is very commonly used in plastic bottles.

The researchers modified regular E. coli bacteria to use PET plastic as an input via Lossen rearrangement, which converts hydroxamate esters to isocyanates, with at the end of the pathway para-aminobenzoate (PABA)  resulting, which using biosynthesis created paracetamol, the active ingredient in Tylenol. This new pathway is also completely harmless to the bacterium, which is always a potential pitfall with this kind of biological pathway engineering.

In addition to this offering a potential way to convert PET bottles into paracetamol, the researchers note that their findings could be very beneficial to studies targeting other ‘waste’ products from biological pathways.

Thanks to [DjBiohazard] for the tip.

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