There is a corner of Antarctica that looks like something out of a David Cronenberg movie. It’s located in the dry valleys of McMurdo, an immense frozen desert where, periodically, a jet of crimson liquid suddenly gushes from the dazzling white of the Taylor Glacier. They’re called the Blood Falls, and since their discovery in 1911 by geologist Thomas Griffith Taylor, they’ve fueled a century of scientific speculation.

Recently, a series of observations conducted since 2018 have clarified several mysteries, such as the nature of their reddish color and what keeps them liquid at almost –20 degrees Celsius. New research published this week in the journal Antarctic Science adds the final piece to the puzzle, clarifying what phenomena drive the falls to gush from underground.

The Science Behind the Blood Falls

At the time of their discovery, Taylor attributed the color to the presence of red microalgae. More than a century later, scientists have determined that the red is due to iron particles trapped in nanospheres along with other elements such as silicon, calcium, aluminum, and sodium. These were likely produced by ancient bacteria trapped underground in the area: Once in contact with air, the iron oxidizes, giving the mixture its characteristic rust color.

As for the presence of liquid water, it is actually a hypersaline brine, formed about 2 million years ago when the waters of the Antarctic Ocean receded from the valleys. The very high salinity of this brine prevents the water from freezing, thus allowing it to gush out periodically.

The New Discovery

With the temperature puzzle solved, the question remained as to what physically drove the fluid to erupt. The answer came from cross-referencing GPS data, thermal sensors, and high-resolution images collected in 2018 during an eruption. The analysis demonstrated that the Blood Falls are the result of pressure variations affecting the brine deposits beneath the glacier.

As Taylor Glacier slides downstream, the overlying ice mass compresses the subglacial channels, building up tremendous pressure. When the strain becomes unbearable, the ice gives way: Pressurized brine seeps into the crevices and is shot out in short bursts. Curiously, this release acts as a hydraulic brake, temporarily slowing the glacier’s march. With this discovery, the mysteries of the Blood Falls should finally have been solved, at least for now. The impact of global warming on this complex system in the coming decades remains unknown.

This story originally appeared on WIRED Italia and has been translated from Italian.

Your smartwatch can track a lot of things, but at least for now, it can’t keep an accurate eye on your blood pressure. Last week researchers from University of Texas at Austin showed a way you smartwatch someday could. They were able to discern blood pressure by reflecting radio signals off a person’s wrist, and they plan to integrate the electronics that did it into a smartwatch in a couple of years.

Beside the tried-and-true blood pressure cuff, researchers in general have found several new ways to monitor blood pressure using pasted-on ultrasound transducers, electrocardiogram sensors, bioimpedance measurements, photoplethysmography, and combinations of these measurements.

“We found that existing methods all face limitations,” Yiming Han, a doctoral candidate in the lab of Yaoyao Jia told engineers at the IEEE International Solid State Circuits Conference (ISSCC) last week in San Francisco. For example, ultrasound sensing requires long-term contact with the skin. And as cool as electronic tattoos seem, they’re not as convenient or comfortable as a smartwatch. Photoplethysmography, which detects the oxygenation state of blood using light, doesn’t need direct contact, and indeed researchers in Tehran and California recently used it and a heavy dose of machine learning to monitor blood pressure. However, these sensors are thought to be sensitive to a person’s skin tone and were blamed for Black people in the United States getting inadequate treatment during the COVID-19 pandemic.

The University of Texas team sought a non-contact s_{olution that was immune to skin-tone bias and could be integrated into a small device.}

Continuous Blood Pressure Monitoring

Blood pressure measurements consist of two readings—systole, the peak pressure when the heart contracts and forces blood into arteries, and diastole, the phase in between heart contractions when pressure drops. During systole, blood vessels expand and stiffen and blood velocity increases. The opposite occurs in diastole.

All these changes alter conductivity, dielectric properties, and other tissue properties, so they should show up in reflected near-field radio waves, Jia’s colleague Deji Akinwande reasoned. Near-field waves are radiation impacting a surface that is less than one wavelength from the radiation’s source.

The researchers were able to test this idea using a common laboratory instrument called a vector network analyzer. Among its abilities, the analyzer can sense RF reflection, and the team was able to quickly correlate the radio response to blood pressure measured using standard medical equipment.

What Akinwande and Jia’s team saw was this: During systole, reflected near-field waves were more strongly out of phase with the transmitted radiation, while in diastole the reflections were weaker and closer to being in phase with the transmission.

You obviously can’t lug around a US $50,000 analyzer just to keep track of your blood pressure, so the team created a wearable system to do the job. It consists of a patch antenna strapped to a person’s wrist. The antenna connects to a device called a circulator—a kind of traffic roundabout for radio signals that steers outgoing signals to the antenna and signals coming in from the antenna to a separate circuit. A custom-designed integrated circuit feeds a 2.4 gigahertz microwave signal into one of the circulator’s on-ramps and receives, amplifies, and digitizes the much weaker reflection coming in from another branch. The whole system consumes just 3.4 milliwatts.

“Our work is the only one to provide no skin contact and no skin-tone bias,” Han said.

The next version of the device will use multiple radio frequencies to increase accuracy, says Jia, “because different people’s tissue conditions are different” and some might respond better to one or another. Like the 2.4 gigahertz used in the prototype these other frequencies will be of the sort already in common use such as 5 GHz (a Wi-Fi frequency) and 915 megahertz (a cellular frequency).

Following those experiments, Jia’s team will turn to building the device into a smartwatch form factor and testing them more broadly for possible commercialization.

In science fiction, the use of gunpowder-based weapons is generally portrayed as something from a savage past, with technology having long since moved on to more civilized types of destructive weaponry, involving lasers, microwaves, and electromagnetism. Instead of messy detonating powder, energy-weapons are used to near-instantly deposit significant amounts of energy into the target, and railguns enable the delivery of projectiles at many times the speed of sound using nothing but the raw power of electricity and some creative physics.

Of course, the reason that we don’t see sci-fi weapons deployed everywhere has arguably less to do with today’s levels of savagery in geopolitics and more with the fact that physical reality is a very harsh mistress, who strongly frowns upon such flights of fancy.

Similarly, the Lorentz force that underlies railguns is extremely simple and effective, but scaled up to weapons-grade dimensions results in highly destructive forces that demolish the metal rails and other components of the railgun after only a few firings. Will we ever be able to fix these problems, or are railguns and similar sci-fi weapons forever beyond our grasp?

The Lorentz Force

The simplest way to think about a railgun is as a linear motor. At its core it consists of two parallel conductors — the rails — with an armature that slides across these rails as it conducts the power between the two rails. This also makes it the equivalent of a homopolar motor, which was the first type of electric motor to be demonstrated.

In the photo on the right you can see a basic example of such a motor, with the neodymium magnet providing the magnetic field and the singular wire the current that interacts with the magnetic field. Using the right-hand rule that was hammered into our heads during high school physics classes we can thus deduce that we get a net force.

With this hand-held demonstration the screw will rotate when current is passed through the wire. For stand-alone homopolar motors with the magnet on the battery’s negative terminal and a conductor loosely placed on the positive terminal while touching the magnet, the Lorentz force will cause the wire to rotate around the battery.

We can visualize this interaction between the current-carrying wire (I), the magnetic field (B) and resulting force vector (F) in such a homopolar motor fairly easy, but how does this work with a railgun?

Rather than a permanent magnet or a complex electromagnet on each rail using many windings, a single current loop is used in a railgun. This means that massive amounts of currents are pumped through one rail, which induces a sufficient strong magnetic field.
The projectile, playing the role of the armature, is located inside the generated magnetic field B, with the current I coursing through the armature, resulting in a net force F that will push it along the rails at a velocity that’s proportional to the strength of B.

Crudely put, the effective speed of a project launched by a railgun is thus determined by the applied current, so unlike it’s close cousin, the coilgun, there is no tricky timing requirement in energizing coils in a sequence.

This also provides some hints as to what major obstacles with railguns are, starting with the immense currents that have to be immediately available for a railgun shot of any significant size. If this is somehow engineered around using massive capacitor banks, then you run into the much more significant issues that have so far prevented railguns from being widely deployed.

Most of this comes down to wear and tear, because going fast comes with certain tradeoffs.

Making Big Stuff Go Fast

Theoretically you can just scale everything up: creating railguns with larger rails and larger armatures that can launch larger projectiles with increasingly faster speeds. This has been the impetus behind various railgun projects across the world, with notable examples being the railguns developed and tested by the US and Japan.

Railguns were invented all the way back in 1917 by French inventor André Louis Octave Fauchon-Villeplée, when the issue of the massive electricity consumption kept further research on a fairly low level. Even the tantalizing prospect of a weapon system capable of firing at velocities of more than 2,000 m/s couldn’t get into deployment during the time that Nazi Germany was working on their own version.

Ultimately it would take until the 1980s for railgun designs to become practical enough to start testing them for potential deployment at some point in the future, seeing a surge of R&D investment for it and other new weapon systems that could provide an edge during the Cold War and beyond.

Yet despite decades of research by the US military, no viable design has so far appeared, and research has wound down over the past years. Although both China and India are testing their own railgun designs, there are no signs at this point that they haven’t run into the same issues that caused the US to mostly cease research on this topic.

Only Japan’s railgun research seems to so far offer a viable design for deployment, but their focus is purely defensive, for countering ballistic and hypersonic missiles in a close-in role. The size is also limited to the current 40 mm prototype by Japan’s Ministry of Defense ATLA agency.

Physical Reality

In a perfect world with zero friction and spherical cows, railguns would be very simple and straightforward, but as we live in messy reality we have to deal with the implications of sending immense amounts of currents through a railgun barrel. A good primer here can be found in a June 1983 report (archived) by O. Fitch and M. F. Rose at the Dahlgren Naval Surface Weapons Center in Virginia.

Much of this comes down to efficiency as you scale up a basic railgun design. The two main factors are basic ohmic resistance (E_R) and system inductance (E_S). These two factors limit the kinetic energy (E_K) and set the losses (E_L) of the system, with the losses being in the form of thermal and other energies.

Reducing these losses is one of the primary points of research, and factors like the rail design and alloys as well as the switching of the current pulses play a role in affecting final efficiency, and with it durability of the railgun’s ‘barrel’.

Naturally, that was all the way back in 1983, and since then a few decades of technical and material science progress having occurred. Or so one might be led to believe, if it wasn’t for current research papers striking a rather similar tone. For example Hong-bin Xie et al. in a 2021 paper as published in Defence Technology.

This review article covers the common issues of rail gouging, grooving, arc ablation, and other problems, as well as the current rail materials in use today and their performance characteristics.

Many of these issues are somewhat related, as the moving armature rarely maintains a perfect contact with the rails. This results in arcing, localized heating, ablation, and grooving due to thermal softening. All of these effects result in a rapidly degrading rail surface, and higher currents result in more rapid degradation and even worse contact with subsequent shots.

Various rail metal alloys have been or are being tested, including Cu-Cr, Cu-Cr-Zr and Cu/Al₂O₃, replacing the pure copper rails of the past. None of these alloys can resist the pitting and other wear effects from repeated railgun firings, however. This has pivoted research towards various coatings that could limit wear instead, such as molybdenum (Mo) or tungsten (W).

Fields of research involve electroplating, cold spraying, supersonic plasma spraying and laser cladding, using a wide variety of coatings. The authors note however that these rail coatings have only begun to be investigated, with success anything but assured.

Defensive Benefits

Quite recently railguns have surged to the forefront in the news cycle courtesy of certain ill-informed fantasies that also involve destroyers which identify as battleships. In these feverish battleship dreams, railguns would act as a kind of super-charged version of the 16″ main guns of the Iowa-class, the last active battleships in history.

Instead of 16″ shells that ponderously arc towards their decidedly doomed target, these railguns would instead send a projectile at a zippy 2-3 km/s towards a target. As tempting as this seems, the big issue is as we have seen of repeatability. The Iowas originally had a barrel life of a few hundred shots before their liner had to be replaced, but this got bumped up to basically ‘infinite’ shots after some changes to their chemical propellant.

A single Mark 7 16″ naval gun fires twice per minute, and this is multiplied by nine if all three turrets are used. The range of projectiles launched included high-explosive, armor-penetrating, and even nuclear shell options, with a range of 39 km (21 nmi) at a leisurely ~800 m/s. To compete with this, a naval railgun would need to be able to keep up a similar firing rate, feature a similar barrel or at least acceptable barrel life, and have a longer range for a similar payload effect.

At this point railguns score pretty poorly on all these counts. Although range of a projectile falls between that of a missile and a Mark 7 naval gun’s projectile, barrel life is still poor, power usage remains very high and the available projectiles at this point in time are basically just relying on their kinetic energy to cause harm, limiting their functionality.

Taking all of this into account, it would seem that the Japanese approach using railguns as a very responsive, close-in weapon is extremely sensible. By keeping the design as small-caliber as possible, reducing rail current, and not caring about range as long as you can hit that hypersonic anti-ship missile, they seem to be keeping rail erosion to a minimum.

Since the average missile tends to perform rather poorly after a 40 mm hole appears through it, courtesy of it briefly sharing the same physical space with a tungsten projectile, this might just be the defensive weapon niche that rail guns can fill.

This Belfast-based company uses machine learning and hyperlocal rainfall forecasting to predict sewer levels, detect blockages and optimise the performance of wastewater networks.

Brian Moloney has spent many years working in the area of environmental engineering.

After obtaining a degree in civil, structural and environmental engineering from Trinity College Dublin, Moloney spent more than 15 years working in drainage and flood prevention, having led major civil engineering projects in Ireland, the UK and Australia.

This civil engineering experience allowed him to see an opportunity for a data-driven approach to tackle pollution and flooding, leading him to co-found our latest Start-up of the Week – StormHarvester.

StormHarvester is a Belfast-based start-up that uses AI to help wastewater utilities better manage their networks and prevent serious flooding and pollution. The start-up achieves this by using AI to monitor rainfall and wastewater networks, providing real-time insights.

“Urbanisation, climate change and population growth are putting huge strain on our water supply systems,” says Moloney. “This is resulting in increased threats of flooding and pollution.

“At StormHarvester, we use machine learning and hyperlocal rainfall forecasting to predict sewer levels, detect blockages and inflow, and optimise the performance of wastewater networks.”

How it works

As Moloney – who is also CEO of the company – tells SiliconRepublic.com, StormHarvester’s initial work focused on understanding the relationship between rainfall and drainage networks.

“Once this was understood, we focused on predicting the future network performance using rainfall datasets,” he says. “After investing time and effort into machine learning, our CTO Stevie Gallagher and I created a quality blockage and anomaly detection product which helped us win our first major competition, winning Wessex Water and beating many established industry analytics providers.”

Today, Moloney says the start-up works with 11 UK wastewater utilities and has onboarded “tens of thousands” of sensors globally.

StormHarvester has released a number of products since its establishment, encompassing a range of areas including inflow and infiltration detection, blockage detection, pump station alerting, rising main alerting and spill verification.

“Our advanced anomaly detection system analyses data from thousands of sensors, turning it into precise, actionable insights that drive smarter decisions,” says Moloney. “Proactive real-time monitoring allows utilities to have visibility over their network, prevent issues before they escalate and move from lagging indicators to live insights.”

How it’s going

To date, StormHarvester has hit a number of milestones.

“In the last year alone, we have doubled our headcount, fueling our expansion and growth strategy further to create exciting opportunities globally,” says Moloney.

According to Moloney, the company has deployed more than 270,000 sensors worldwide, and in January 2025, StormHarvester announced plans to double its workforce over three years and expand into new countries after raising £8.4m in Series A funding.

Meanwhile, in December, StormHarvester was named as Ireland’s fastest-growing technology company at the annual Deloitte Technology Fast 50 awards, which ranks Ireland’s 50 fastest-growing tech companies based on revenue growth over a four-year period.

But while the company experienced rapid scaling, Moloney says this introduced a challenge for the team.

“As we grew, we hired quickly, introduced more structure and refined processes while trying to keep culture and communication consistent,” he explains. “Balancing fast growth with maintaining alignment was a challenge.”

Currently, Moloney says the company is planning further expansion. He says the start-up’s successful move into Australia and New Zealand has shown that StormHarvester can “scale sustainably while keeping our culture and quality intact” – adding that the company is now preparing for entry into the US market.

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Panasonic has announced its full 2026 European TV lineup, headlined by new OLED models and a expanded Mini-LED range.

The new series focuses on brighter panels and larger screen options up to 86-inches. In addition, it offers improved viewing in well-lit rooms thanks to new Glare Free technology.

The range spans OLED, QD Mini-LED, QLED, 4K LED and 2K LED. Smart platforms vary by region and model — including Fire TV built in, Google TV, Roku and TiVo. Notably, 2026 marks Panasonic’s first Roku-powered models in the UK.

Nvidia has become shorthand for the AI market itself. In the years since generative models reshaped computing, the company’s GPUs have powered everything from large-scale training clusters to real-time inference infrastructure.

That dominance helped Nvidia’s stock surge over 1,500 percent from 2022 into 2025 and made it one of the most valuable tech firms in history.

Yet as its newest earnings report approaches, investors aren’t just asking whether revenue is growing, they’re asking whether the AI boom still has room to run.

Scaling AI isn’t just about silicon anymore

Analysts expect Nvidia to post another blockbuster quarter, with revenue forecasts between roughly $65 billion and $66 billion and adjusted gross margins near 75 percent.

That kind of performance would mark continued strength in demand for high-end AI accelerators, particularly from cloud providers and hyperscalers that underpin much of the industry’s infrastructure.

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On the surface, those numbers look almost routine at this point, after all, Nvidia has beaten estimates for revenue and earnings for more than a dozen straight quarters. But markets have shifted, and so has investor psychology.

The question now isn’t just “how much growth?”, but “for how long?” and “toward what?”

One reason for that shift is the growing push by major AI users to develop or adopt alternatives to Nvidia’s hardware.

Meta, Google and other hyperscalers are investing heavily in custom silicon or alternative accelerators designed to cut costs, optimize specific workloads, or gain strategic independence from Nvidia’s ecosystem.

Those moves don’t immediately undercut Nvidia’s sales, but they signal a longer-term competitive environment that didn’t exist a few years ago.

This isn’t entirely new, the chip industry has always been cyclic and competitive, but it matters more now because so much of global AI infrastructure hangs off a single architecture. When customers start hedging that exposure, it naturally ripples through valuations and strategic forecasts.

Investor expectations are part of the story

Another reason this earnings cycle feels different is the backdrop in broader markets. AI names have led the rally in tech stocks, but sentiment has softened.

Over the first weeks of 2026, Nvidia’s share price has barely budged compared with steep gains in previous years, even as other industries waver under economic uncertainty.

Some analysts read this as a sign that markets are increasingly focused on profitability timelines and real-world deployment metrics rather than narrative alone.

Part of that recalibration reflects broader anxiety about what some observers call an “AI bubble,” where valuations in the sector may be disconnected from underlying economic fundamentals.

Whether or not that label is fair, it reflects genuine investor nervousness about sustainability, return on investment, and how soon large companies will convert AI hype into consistent revenue growth.

What Nvidia can and must deliver

For Nvidia, this means earnings won’t be judged simply on topline figures. The market will be listening closely to a few specific signals:

• Demand trajectory from hyperscalers and cloud providers. Are capex cycles still accelerating, or showing signs of plateauing?
• Guidance on future quarters. Vague or cautious outlooks could spook markets that have priced high growth into Nvidia’s valuation.
• Comments on competitive strategy, particularly around partnerships, software ecosystems, and how the company plans to respond to custom silicon trends.
• Supply chain and geopolitical risks, including memory pricing and export restrictions that affect where Nvidiacan sell its most advanced chips.

A strong earnings beat with confident guidance could reassure markets that AI spending isn’t slowing and that Nvidia remains the core engine of that demand. A modest beat or mixed signals, however, might validate some of the more cautious narratives and lead to broader tech sell-offs.

Nvidia’s report matters because it has become the default bellwether for AI infrastructure spending, and by extension, for how investors value growth in technology sectors.

If the company shows that demand and pricing power remain robust, it supports a broader bull case for AI adoption. If not, we may see a re-rating of AI as an investment theme, with implications far beyond one company’s earnings call.

In that sense, this quarter isn’t just about chips or quarterly revenue. It’s about confidence: in AI’s staying power, in enterprise capex cycles, and in the narrative that has driven one of the most remarkable growth stories in recent market history.

You can find the financial report here.