Swabbing the World's Cities for Coronavirus
https://spectrum.ieee.org/swabbing-the-worlds-cities-for-coronavirus

Emily Waltz is the power and energy editor at IEEE Spectrum.

In cities around the world, an army of researchers has been busy swabbing mass transit kiosks, public benches, hospital floors and other high traffic urban surfaces. They’re looking for genetic evidence of SARS-CoV-2, the virus that causes COVID-19, and using the data to build computational models that could warn us of impending viral outbreaks.

“Instead of reactive epidemiology, it’s proactive tracing,” says Christopher Mason, a professor at Weill Cornell Medicine, who oversees the project. “If you did a simple targeted capture of known viruses once or twice a week in every city, you could catch an outbreak well before it becomes a big problem.”

The project, dubbed METACoV, spans 15 cities, including New York, Sao Paolo, Hong Kong, Tel Aviv and Berlin. It’s one of several such  swabbing ventures by the international consortium Metagenomics and Metadesign of Subways and Urban Biomes, or MetaSUB. Since 2016, the organization has been collecting genetic samples from more than a hundred cities and studying what kind of microbial life exists on our most shared surfaces.

When the pandemic hit, MetaSUB’s global network got to work. The research groups send their postdocs, grad students, and medical students out into their cities to collect the samples. At public transit hubs, for example, they target features universal across nearly all cities: kiosks, benches, and turnstiles or other entry barriers. Then, for three awkward minutes, the researchers brush the surface with a nylon swab that looks like a Q-tip and is confirmed to be DNA and RNA free.

Weill Cornell associate professor Christopher Mason and medical student Heba Shaaban take samples from a kiosk in a New York City subway station.CHRISTOPHER MASON

“Three minutes is the perfect balance between getting enough yield of DNA or RNA and minimizing the amount of social discomfort,” says Mason. “Pre-COVID, it always looked weird to be out there swabbing with gloves and a mask on, but the pandemic has really normalized our behavior.”

The researchers place the samples in a storage medium that preserves the genetic material, and then transport them back to the lab where the DNA or RNA is extracted, prepped, and sequenced. They use a range of bioinformatics tools to identify what viruses or species are present, and with what mutations.

The data enable researchers to create an atlas of sorts that shows where high risk variants of SARS-CoV-2 are emerging. This can help public health officials stay on top of the spread of the virus, and make decisions on where to focus vaccine supplies. Computational models also indicate where the virus might be headed genetically and geographically.

The data have yielded useful public health information. SARS-CoV-2 hasn’t shown up much on public transit surfaces, Mason says. But it is plentiful on hospital surfaces, including in rooms where people are negative for COVID-19, the researchers have found.

The team tested hospital samples to see how readily they could infect human cells. Luckily, none of the samples were successful. “It’s sitting on a hard surface that may have been bleached the night before. It’s a rough environment for anything to try to survive,” says Mason.

They also found SARS-CoV-2 on the ceilings of hospital restrooms. (If you’re eating while reading this, now would be a good time to put down your food.) How the virus reaches these ceilings is unclear, but Mason hypothesizes that it’s the result of what he calls “fecal fallout.” A COVID-19 positive person uses the toilet, flushes, and since the virus survives in stool and the toilets don’t have lids, the virus gets aerosolized and hits the ceiling. “It’s totally gross but probably true,” he says.

No wonder the consortium sends students to do the field work.

But it’s important work. In fact, stool may turn out to be a powerful predictor of outbreaks. As part of the METACoV project, the consortium is surveying city sewer systems, and what they are finding is fascinating: The amount of virus found in sewage typically peaks five to six days before confirmed cases peak in a population.

The National Institutes of Health (NIH) this year awarded a $5 million grant to Mason and his colleagues to support their wastewater surveillance project. The goal is develop an early warning and viral mapping system for genetic variants of SARS-CoV-2.

A paper summarizing the data collected so far from city centers is in press in Cell, Mason says, and data collected from hospitals is described in a paper published this month in the International Journal of Environmental Research and Public Health. The consortium continues to collect data from these environments. They are also studying the presence of the virus in mosquitoes and cats.

Mason’s team first published on the topic of urban metagenomics in 2015, when they analyzed the microbial ecosystem of the New York City subway system. Fun fact: They found that almost half of all DNA present on the subway’s surfaces matched no known organism.

is the power and energy editor at Spectrum. See full bio →

It aims to lift the industry with its aircraft, motors, batteries, and charging network

Beta Technologies, one of the top three U.S. electric aircraft makers, is aiming first at regional cargo and medical transport, before pursuing the passenger market. Shown here is Beta’s Alia A250, for vertical takeoffs and landings.

In the cockpit of Beta Technologies’ Alia CX300, pilot Chris Caputo steers onto Runway 33 at Patrick Leahy Burlington International Airport in Vermont. Cleared for takeoff, he releases the brakes and nudges the throttle forward.

At the back of the aircraft, the five-blade pusher propeller snaps awake, and the Alia bolts forward. There’s no shriek of a turbofan spooling up, no shudder of a piston engine, and no wait for thrust. Just the low hum of an electric motor and a shove of instant acceleration.

Within a few seconds, we’re at 85 knots (almost 160 kilometers per hour), and Caputo rotates the nose upward. The runway drops abruptly away as we climb. Gaining speed and altitude, we head northwest over Lake Champlain. Through the wraparound canopy, I watch the hunched shoulders of Vermont’s Green Mountains shrink under the right wing, with New York’s taller Adirondacks to the left.

Caputo, a former Air Force fighter pilot, flicks the control stick hard left, then right. Despite its 15-meter wingspan, the Alia feels snappy, its fly-by-wire system automatically coordinating aileron and rudder inputs. The machine, he says, is much simpler to manage than the  Cessna 182 I flew around Australia in my student-pilot days.

What’s more, our half-hour flight in the Alia will consume only about US $9 worth of electricity. The same flight in a comparable Cessna 208 Caravan would burn roughly 25 to 30 gallons of Jet A fuel, costing about $210 to $255 at press time. “Even at full throttle, we’re not burning any fuel up here,” says Caputo, as he takes the Alia into a sweeping left turn and heads south over the lake. “We’re not polluting the environment. [There’s] no noise pollution.”

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At Beta’s South Burlington, Vt., facility, workers assemble the body of an Alia A250. The company says it has an order and option-to-buy backlog of nearly 1,000 aircraft.Beta Technologies

This ride is happening during an unusually lively time for electric aviation. The promise of lower operating costs and cleaner flight has pushed electric aircraft from a fringe experiment to one of the aviation industry’s most closely watched frontiers. After years of renderings, prototypes, and overly optimistic timetables, companies are now flying full-size aircraft, submitting hardware to regulators for certification, building factories, and testing routes with prospective customers.

Among the top three U.S. developers of electric planes, Archer Aviation and Joby Aviation are concentrating on piloted vertical-takeoff-and-landing air taxis, otherwise known as eVTOLs. Beta Technologies is taking a broader—and in some ways more innovative—path into the market. Rather than betting first on air taxis, Beta is looking to enter electric aviation through short-haul freight planes that use conventional runways, fed by an open charging network that’s designed to make electric aircraft—both Beta’s and its competitors’—part of a working transportation system.

To that end, the company is developing two variants of its Alia airframe: the runway-based CX300 I’m flying in and the A250, which adds four lift propellers for vertical takeoff and landing. Beta says its test pilots have flown the Alia family more than 300,000 kilometers, including doing multiple U.S. crossings and overseas demonstrations. The company claims an order and option-to-buy backlog of 991 aircraft.

RELATED: From Hockey Enforcer to High-Flying eVTOL CEO

“We’re taking a pragmatic stepwise approach for both certification and market entry,” says Beta’s CEO and founder Kyle Clark. “We’re ready to go today with medical and cargo aircraft that fly from airport to airport.” Next up, he says, will be passenger aircraft with conventional takeoff and landing, followed by cargo eVTOLs, and finally passenger eVTOLs.

Beta’s 17,500-square-meter production facility sits beside the Burlington airport and looks more like a giant sunlit workshop than a conventional airplane factory. Geothermal heat pumps help heat and cool the structure. The roof will eventually hold a megawatt of solar panels. On the floor, technicians make composite structures and join aircraft sections beneath American and Pride flags.

The top three U.S. electric-aircraft makers are taking different approaches to the same challenge: how to make short-hop flight cleaner, quieter, and commercially useful.

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Kyle Clark, a native of Vermont, founded Beta in 2017 to realize his long-held dream of building a hybrid-electric aircraft. (For his bachelor’s thesis at Harvard University, he designed a flight-control system for such an aircraft.) Martine Rothblatt, CEO of the biotech company United Therapeutics and cofounder of SiriusXM satellite radio, invested $48 million in Beta to fund its first prototype, with the eventual goal of using the aircraft to transport organs for transplantation.

The company has kept much of its design and manufacturing in-house, partly because the pieces are closely coupled and partly because suitable aviation components didn’t exist when development began. Vertical integration gives Beta’s engineers control over the interfaces and any failure data. But it also means the company must be competent at several businesses simultaneously: airframes, electric motors, inverters, batteries, software, chargers, and aircraft production.

The production line can build both the fixed-wing CX300 and the vertical-lift A250, which share the same fuselage, wings, pusher motor (for horizontal propulsion), batteries, avionics, and much of the tooling. This results in some one-size-fits-all decisions: The CX300’s twin booms, which connect the wing to the tail surfaces, look superfluous until you see the A250’s four lift propellers mounted on similar structures.

On the CX300, those booms add weight and drag even though they serve no lift function. But the trade-off—a simpler overall design—is part of Beta’s central strategy: Rather than applying for FAA certification for the more novel and complex eVTOL A250, with its multiple lift systems and transitions between hover and wing-borne flight, the company is bringing the runway version forward first. It’s certifying the motor, batteries, fly-by-wire controls, and much of the airframe in the simpler aircraft; it will then reuse evidence and manufacturing experience when it certifies the A250.

The other big reason to start with the CX300 is logistical: The United States has more than 5,000 public-use airports, so the CX300 can operate from infrastructure that already exists. The cargo and medical flights that the aircraft will initially tackle offer tightly defined missions in which lower energy and maintenance costs matter more than cabin amenities. Hence the company’s decision to leave cabin air-conditioning off the CX300, at least for now. “Things that don’t exist on an airplane don’t weigh anything,” Clark says. “They can’t break. You don’t have to service them…. You don’t have to certify them.”

“This strategy, at the beginning, was seen as a failure of their VTOL,” says analyst Sergio Cecutta, a managing partner at SMG Consulting. “But I think a lot of people were very myopic. It was a way for [Beta] to break up the certification in two pieces, and so they have retired a lot of the risk. [A majority] of the [CX300’s] parts are shared with the VTOL, and that means that a lot of the documentation for certification can be easily reused on the VTOL side when that certification starts.”

A decade and a half after electric cars began making significant inroads into the automotive market, electric airplane technology is advancing rapidly. In some ways, an electric car has more complicated power-engineering requirements than an electric aircraft. An electric car’s power demands change constantly, while an electric aircraft operates in a narrower set of power regimes: takeoff, level flight, and descent.

But if your car’s drivetrain fails, you can just pull over to the side of the road. That’s not an option for a pilot 1,000 meters up. The hurdle isn’t that electric motors are inherently fragile; it’s that aviation safety standards are extraordinarily demanding. Catastrophic failure conditions generally must be held to roughly one chance in a billion flight hours or less.

The two versions of Beta’s Alia aircraft—the A250 [shown] and the CX300—share many of the same components, which the company believes will speed up FAA certification.

Because individual electrical components cannot meet that standard on their own, electric aircraft rely on layered redundancy: duplicate power paths, independent inverters, fault isolation, monitoring, and backup capacity. Each layer helps close the safety gap, but each also adds weight and complexity.

To close the reliability gap and satisfy regulators who have little experience with electric aircraft, Beta engineers have built redundancy into the motor itself. When I visit the company’s motor shop, technicians are assembling the H500A pusher motor, which splits up critical components like the inverters and stator sections.

“You could lose an entire half of the system, and it would still function the same,” says Adam Lowe, Beta’s flight operations lead.

The H500A’s broad diameter gives the magnetic forces greater leverage, producing high torque at low rotational speed, a useful trait when turning large propellers. The H500A weighs about 75 kilograms and produces about 640 kilowatts of power. The motor achieves 97.5 percent efficiency, based on internal testing; typical conventional turboprops have an efficiency range of 25 to 35 percent.

Beta had aimed to have its motor certified in the first half of 2026, a deadline that passed without a public announcement. Although the company’s certification strategy is aimed at streamlining the process, getting clearance to fly from the Federal Aviation Administration is probably still its biggest obstacle. For Beta, and any company trying to bring an electric aircraft into service, the FAA must sign off first on the propeller, then the motor, then the aircraft. After that, the manufacturer must pursue a production certificate from the FAA, proving that the company can build the same approved aircraft again and again, with the right tools, records, inspections, quality controls, supplier oversight, training, calibration, and parts tracking.

Beta has found a narrow on-ramp to get the A250 flying before full certification. Under the FAA’s eVTOL Integration Pilot Program, companies and public agencies can run FAA-supervised, real-world trials to test how advanced air-mobility aircraft might operate in the national airspace. For Beta, that included July flights across a Virginia-to-Maryland corridor carrying, among other things, a manufactured transplantable organ in a medical containment system. Beta has also been chosen for six other FAA-backed trials.

For the fixed-wing Alia CX300, customer trials are moving in parallel. In March, Beta’s CX300 successfully completed 23 flights over 10 days on Loganair routes linking six Scottish cities. The airline has since signed a term sheet for five Beta aircraft, with options for five more and service expected to start in 2029.

Beta is pushing so hard for the A250 because the case for electric flight gets even more interesting with vertical-lift aircraft. Helicopters and other VTOLs have a less orderly life than fixed-wing planes do. They must hover, sidestep, turn in place, settle, climb, and hold themselves in the air by main force. But conventional helicopters are built around a rotor speed that varies only a few percent because the engine, transmission, and rotor system are all designed around one narrow operating point.

“One of the dreams of vertical flight has been to vary the rotor rpm,” says Anubhav Datta, an aerospace engineering professor at the University of Maryland, College Park, who studies electric propulsion and vertical flight. Electric motors, he argues, have advanced enough to make that practical. A rotor that can slow down or speed up as conditions change can waste less energy, make less noise, and better match the odd assortment of tasks that vertical flight demands.

Electric propulsion also lets designers break up the work with multiple motors. The weight of an electric motor is tied closely to its torque. If you spread that torque among several motors, each of the motors can shrink, meaning that their combined weight is less than would be needed for one big engine. This is one reason why so many electric aircraft designs appear to have sprouted propellers wherever an engineer found a place to put one.

There is a downside to using multiple smaller motors: If one of the motors quits, the rotor it’s driving has little stored energy compared to the inertia in a conventional helicopter’s one large rotor. This inertia can come in handy if the engine dies, because the rotor will continue to spin and allow the pilot to descend under control, in an emergency technique known as autorotation.

According to Beta Technologies founder and CEO Kyle Clark, his native Vermont—known more for its maple syrup and outdoorsy lifestyle than its technological achievements—is a big part of the secret to Beta’s success at attracting talent. “The recruiting strategy is this: You find anybody who has vacationed in Vermont or went to school here. And you say, ‘You can actually live here and have a high-paying job in a mission-driven business, doing something that you care about with great people.’ ” The company currently employs about 1,500 people, most of them in South Burlington, Vt., and is actively hiring for its engineering, manufacturing, software, flight operations, and commercial teams.

As befits an aviation company, Beta offers flying lessons for all employees—a perk that is intended to give engineers, software developers, pilots, and mechanics a common language. The company also operates a health clinic for employees and their families and is building a day care center. Those programs have an idealistic Vermont flavor that is shaped by Clark and his wife, Katie Clark, who is Beta’s head of brand, creative, culture and spaces. But they also serve a practical purpose: attracting and retaining the best people in a small labor market. —T.C.

Electric-aircraft makers compensate with redundant motors and electrical channels, but most leading designs also have wings to allow gliding. In Beta’s A250, that means that if all else fails, the pilot still has a shot at reaching a suitable landing spot.

That helps explain the convergence in today’s eVTOL designs. Joby Aviation’s S4 uses six propellers that tilt between lift and cruise. Archer Aviation’s Midnight has six tilting propellers and six dedicated lift props. Beta’s A250 keeps its four lift propellers horizontal and uses a separate pusher motor for forward flight.

In Beta’s home state of Vermont, the electricity produced in the state comes almost entirely from renewable sources. Of course, electric aviation’s climate advantage depends not only on how clean the power grid is, but on how efficiently the aircraft uses energy. The turbines in conventional jet engines are marvelous machines, but they throw away a lot of energy as heat. Plus they need to carry the hardware to manage that heat—inlets, ducts, radiators, oil coolers—and the drag that comes with pulling air through all of it.

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Kyle Clark [in cockpit], founder and CEO of Beta Technologies, pilots the company’s Alia CX300, an electric plane that Beta expects to receive FAA certification in 2027. Clark began designing a hybrid electric plane while still in college.Beta Technologies

By contrast, an electric design, with no combustion, gearbox, or oil circuits, avoids much of that energy burden. The result is an aircraft that is more than twice as “slippery” in terms of its lift-to-drag ratio. Batteries will never come close to the fuel density of petroleum fuels. But because electric drivetrains are much more efficient than combustion engines, a battery doesn’t necessarily need to match the energy density of petroleum fuel to be useful.

While the motors on an electric aircraft provide the drama at takeoff and landing, the batteries set nearly every boundary: the range, payload, charge time, cooling load, reserve energy, and, ultimately, useful life. For now, the hardest limit on electric aviation is not imagination, software, or even motors. It is the stubborn weight of stored energy.

“One thing you notice about [electric] airplanes is that they are all small,” says Cecutta, the aviation analyst. “They are not small because someone has done a market study and decided that is the optimal size. They are small because you cannot make them big. This limitation is from the batteries.”

In a conventional aircraft, fuel accounts for a significant percentage of the takeoff weight. But that weight gradually disappears during flight. In an electric plane, the battery packs also account for a lot of the weight; in Beta’s CX300, they’re close to half the aircraft’s mass. But a drained battery at landing weighs the same as a fully charged one at takeoff. That means the rest of the aircraft must be light enough to lift a power source that never gets lighter, yet strong enough to endure vibration, turbulence, hard landings, and years of fatigue. The more engineers push to save weight, Datta notes, the more flexible the airframe becomes, making the wings and other structures more prone to bending, twisting, and vibration. In that sense, the battery problem becomes a structural problem, too.

Beta’s CX300 carries five liquid-cooled battery packs of about 45 kilowatt-hours each, giving the aircraft roughly 225 kWh of rated capacity, though not all of that may be usable in flight. Each pack weighs 255 kg and contains 2,772 cylindrical cells. Altogether, about 14,000 cells sit beneath the occupied cabin, each one a tiny storehouse of energy, heat, and potential trouble.

The burden of the battery explains much of what I see elsewhere in Beta’s aircraft: the 15-meter wingspan, the composite structure, and the almost doctrinal effort to leave out every piece of hardware and creature comfort that’s not essential to the mission. The CX300 even does without flaps, which most aircraft use to generate extra lift at low speeds, so it must take off and land unusually fast for an aircraft of its size.

On Beta’s battery line, commercially supplied standard 21-millimeter-diameter, 70-mm-long cells are tested and assembled. Cells become modules, modules become subpacks, and eleven subpacks become a 45-kWh pack. Cooling plates manage heat during normal operation, while passive barriers and electrical isolation are designed to contain the ugly edge cases that Beta’s testing process intentionally triggers.

The modular design of the packs will give Beta’s planes a path to better batteries as the cells and battery chemistry improve. Caputo tells me that the company is already evaluating higher-density cells at its battery testing facility in nearby St. Albans, Vt. More watt-hours per kilogram could eventually mean more range, more payload, or both.

What sets Beta Technologies apart from its competitors is the breadth of its ambitions: It’s not just building electric aircraft. It’s also cultivating a broader ecosystem to nurture the growth of electric aviation.

For instance, Beta intends its motor business to extend beyond its own aircraft. The company has delivered lift motors for Eve Air Mobility’s proof-of-concept aircraft and has a contract to supply up to $1 billion in pusher motors as Eve’s program develops. In addition, Beta is selling flight-control computers and software to Horizon Aircraft. It is also producing propulsion systems for the military, as well as the MV250, a hybrid-electric autonomous aircraft for defense missions.

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Beta’s fast-charging stations [left] have been installed at 138 locations. The thermal-management station [right] keeps the battery from overheating during charging. The company uses the open-standard Combined Charging System (CCS) so that other aircraft can use its chargers. Beta Technologies

Further evidence of Beta’s far-ranging plans is visible near the battery line in the South Burlington facility, where workers are readying a few of the company’s 1.5-meter-tall Charge Cubes for shipping. Intended for installation at airports, each cube takes 480-volt AC from the grid and can deliver up to 320 kW at as much as 1,000 volts DC. According to Beta, charging either model of its Alia aircraft takes about 35 minutes, from the minimum charge required under visual flight rules to full state. A separate thermal-management cube circulates water-glycol coolant through the aircraft to keep the batteries from overheating during charging.

The charge cubes use the Combined Charging System (CCS), an open standard increasingly shared across the electric transportation industry. Beta’s strategy resembles Tesla’s decision to treat charging as part of the vehicle business, with one important difference: Beta is courting other aircraft makers, airports, and operators to use its technology. Archer, Atlantic Aviation, Signature Aviation, and government agencies have purchased or installed Beta’s charging equipment. As of October, Beta has 138 fast-charge locations, with additional sites in permitting or construction.

For charging beyond the fixed network, Beta offers a wheeled 40-kW Mini Cube and a 65-kW Mini Cube+ that can ride inside an Alia and plug into more modest electrical service. The Mini Cube can also charge cars, buses, and trucks—any vehicle that uses the CCS standard.

Back above Lake Champlain, the CX300 levels off, and the motor recedes to a faint hum. Then Caputo switches it off, and there’s nothing but the rush of wind around the canopy. The aircraft glides on its long wings while the coasting propeller turns the motor into a generator, feeding about 9 kW back to the battery.

Then we turn to the east and slip into the landing queue behind a National Guard C-130 Hercules, and Caputo powers and slides the throttle forward. The motor springs to life again, pushing the airplane, and perhaps aviation itself, into the future.

How VicOne helps expose cyber risks in AI robot safeguards

is a Physical AI cybersecurity company protecting vehicles and robots from cyber threats that can alter behavior and compromise safety.

A robot can follow its safety rules and still act on manipulated information. See what this means for robot safety and how teams can strengthen their testing and evidence.

This article is brought to you by VicOne.

A mobile robot slows for someone in a hallway. A collaborative arm eases its movement as a worker approaches. A humanoid pauses to let a person pass. Each response depends on information about the robot or its surroundings: a distance reading, a position estimate, or a stop signal.

But what happens when one of those inputs is wrong, and the robot still responds exactly as designed? The mobile robot reads the hallway as clear while someone is still there. The arm estimates that the worker is farther away. The humanoid does not receive a stop signal as a person crosses its path.

In each case, a fault could cause that mismatch. So could deliberate manipulation.

At VicOne LAB R7, we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people safe.

A robot does more than measure its surroundings. It may interpret what it sees and hears to decide what to do next. Information placed in its environment can therefore influence its actions.

Published research shows how this can happen. In a study of vision-language-action (VLA) models, a patch in a camera’s view reduced task success in simulated robot tests. FreezeVLA found that an adversarial image could cause tested models to ignore later instructions. Although the studies used different methods, both demonstrate how visual input can interfere with a robot’s intended task.

In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement.

VicOne LAB R7 also tested how untrusted inputs could change robot behavior. In a robot-dog test using Gemma 4 E4B, text on a poster was treated as an instruction and changed the robot’s movement. In a separate hospital-service-robot simulation using Nemotron on NVIDIA Jetson AGX Orin, crafted audio that is inaudible to people changed the robot’s simulated behavior. The assigned tasks did not change; the inputs did.

In a VicOne LAB R7 test, text on a poster changed a robot dog’s movement, illustrating how a crafted input can turn an unlikely outcome into a repeatable attack.VicOne

An independent protective system may still stop a dangerous action. But that system also depends on information: readings, estimates, and messages that tell it when to intervene. What happens if one of those inputs is manipulated?

At a robotics bug bounty event, VicOne LAB R7 researchers injected a ROS 2/DDS message into a robot that organizers expected to remain still under a safe-control setting. The robot moved.

A humanoid’s balance controller offers another example. If it relies on a vulnerable gyroscope, sound at the sensor’s resonant frequency could distort its reported rotation. The controller might correct for a tilt that never occurred. Researchers demonstrated the underlying acoustic attack against drones with vulnerable gyroscopes. The research did not demonstrate the same attack chain causing a humanoid to fall.

Other protections could interrupt either chain. The question for safety and security teams is whether those protections detect the danger independently or depend on the same manipulated information.

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 Figure 1. VicOne maps how accidental faults and deliberate cyber manipulation can lead to similar robot safety outcomes, from loss of control to physical harm.VicOne

A safety assessment may treat a combination of accidental faults as unlikely. Deliberate manipulation changes that assumption. An attacker can choose when to introduce a false input and repeat the same trigger. That does not make harm inevitable, but it may change how cyber risks should be assessed.

Redundant sensors also need an adversarial test. If one action can affect both inputs, their agreement may offer less reassurance than expected. In autonomous driving research, a crafted physical object misled a system combining camera and LiDAR perception. The study does not establish a weakness in a particular robot, but it shows why teams should test whether one manipulation can affect several checks at once.

Cyber threats add intent to the safety equation, so risk assessments based on accidental faults must be revisited to account for attacks that can be timed and repeated.

Safety teams set limits for speed, protective distances, and permitted operating areas. The evidence behind those limits should also show what happens when the information used to enforce them is deliberately manipulated.

That evidence can also support assessments against applicable requirements. China’s GB/T 45502-2025 addresses information security for service robots. IEC TS 63074 examines security threats that could affect safety-related control systems. The EU Machinery Regulation, which applies from January 20, 2027, includes requirements to protect safety-relevant systems and data against corruption. Each has its own scope, and teams still need evidence for their robot’s design and operating conditions.

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 Figure 2. Cybersecurity strengthens robot safety evidence throughout the lifecycle. VicOne helps teams test safety limits against manipulation before deployment and monitor the conditions behind safe behavior in operation.VicOne

Before deployment, safety and security engineers can compare normal and adversarial scenarios against the same safety limits. During operation, they can monitor for changes to software, models, sensor signals, or behavior that may call earlier results into question. Any response to suspicious behaviors should follow policies approved by the safety team.

VicOne supports that work across the robot lifecycle. Through the Robotic Hacking Community, VicOne LAB R7 works with researchers to investigate how cyber threats can change robot behavior. VicOne’s Radeis helps teams validate potential effects before deployment, while Rthena provides visibility into risks and behavior during operation.

The central test is whether a robot’s safeguards still protect people when the information they rely on is manipulated. Safety and security teams can begin by tracing the inputs behind each protective decision, challenging them, and revisiting the evidence as the robot changes. They can start with these five questions:

1. What does each protective function read? Map the readings, estimates, messages, and confirmations. Identify which safeguards operate independently of the robot’s AI decisions.

2. How does that information arrive? Check how inputs are produced, transmitted, authenticated where appropriate, and handled when they are missing or implausible.

3. What happens if an input is manipulated? Test false distances, drifting position estimates, and other relevant scenarios against the robot’s safety limits.

4. Can one action mislead several inputs? Check whether sensor fusion or seemingly independent safeguards share a point of failure.

5. When should the evidence be revisited? Reassess after changes to software, models, sensors, or operating conditions. Agree on bounded responses to suspicious behavior with the safety team in advance.

The goal is not only to show that a protective function works. Teams also need evidence that it remains protective when the information behind its decision is wrong or deliberately manipulated — and that another safeguard can catch the resulting hazard when needed. As robots change, that evidence needs to change with them.

For a deeper look how cybersecurity can affect robot safety, download our white paper “Why Robot Safety Depends on Cybersecurity: Protecting the Information That Safety Decisions Rely On.”

How edge AI and physical AI help cities move beyond passive monitoring

is an American multinational technology company headquartered in Santa Clara, California.

This white paper explains how edge AI and physical AI help cities move beyond passive monitoring. It shows how urban infrastructure can sense, reason, and act on real-time conditions across public safety, mobility, and infrastructure management.

Machines can automate drudgery but engineers make key decisions

is an IEEE member, a research affiliate at MIT, and a senior leader at Google in Sunnyvale, Calif.

Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility.

AI can help engineers do early project steps that once required several people with different technical skills. The engineer’s role is therefore expanding from performing each technical step to deciding how people, machines, and specialists’ knowledge should work together on the project.

Engineering has always been divided into disciplines, and for good reasons. Electrical, mechanical, civil, software, and other engineering fields require deep knowledge that takes years to develop. AI does not replace that need. It simply changes how easily engineers can explore the parts of a problem that sit outside their own specialty.

Consider a software engineer working on a medical device. AI can help the engineer review relevant biomedical research, understand unfamiliar terms, analyze data, and build an early model.

That does not make the engineer a physician or biomedical expert. It allows the engineer to ask better questions before working with the experts.

The same is true in energy, transportation, manufacturing, and many other fields. A problem might begin as a software issue and then quickly involve hardware, safety measures, economics, human behavior, and regulations.

Engineers do not need to master every field; they need to recognize which expertise the problem requires and bring the right people and tools together.

Systems thinking thus becomes especially important. It requires looking at the whole problem: understanding how its parts affect one another, identifying constraints and failure points, and making trade-offs.

AI adds a new element to the process. Engineers must decide not only how the technical parts fit together but also what work to give to AI, what a person must check, when a specialist should make a decision, and when the problem needs to be redefined.

An engineer’s value is not in knowing everything. It is in knowing how to frame the problem, how to ask the right questions, how to test the answers, and when another expert must lead the project.

This aspect is crucial because technical challenges in engineering are seldom static. As new insights emerge, objectives might shift, unseen limitations can surface, and the initial premise might prove flawed. Rather than rigidly following the starting specifications, an effective engineer adapts and refines the scope as a deeper understanding of the system develops.

Thus, engineering extends beyond mastering technical concepts; it is an adaptable, structured approach to navigating evolving challenges.

New evidence can change the goal, reveal a hidden constraint, or show that the original hypothesis was wrong. Effective engineers go beyond merely acting on the initial problem statement. They update it as the system becomes better understood.

In that sense, engineering is not only a body of technical knowledge, it is also a disciplined way of thinking about a project.

AI can produce convincing answers even when its assumptions are weak. It can generate code that works in one test but fails in another. It can summarize research without understanding which evidence is strongest, or optimize a design for the wrong goal. Such limits make human expertise more important, not less.

Machines are useful where speed, scale, search, pattern recognition, and data generation matter. People are essential where context, values, responsibility, and judgment matter. The challenge is to decide where each belongs.

An AI system might generate several designs for a medical sensor, for example, and predict how each will perform. An engineer can test the predictions and reject designs that violate technical requirements. A clinician can determine whether the sensor can be realistically used. Patients and practitioners might reveal problems that neither the engineer nor the developer considered. The final solution emerges from combining the perspectives.

The engineer is still a builder and specialist but increasingly also a coordinator of knowledge.

The broader form of engineering creates an important role for IEEE. Its societies already bring together expertise in various technologies, engineering education, technical standards, and other fields. That makes IEEE well placed to help engineers work across specialties without weakening the value of specialization.

IEEE could create more communities organized around problems that require expertise from several fields. Examples include trustworthy clinical AI, resilient infrastructure, energy reliability, scientific discovery, and safe autonomous systems. An engineer could begin in a home discipline and then work with specialists from other IEEE communities as a project requires.

IEEE also can help set practical standards for human-machine collaboration. Engineers will need clear guidance on when AI-generated work must be checked, how uncertainty should be documented, when a specialist must make a decision, and who is responsible when a system uses both human judgment and machine output.

Those questions are becoming part of everyday engineering. They should not be left to each company or project manager to answer independently. A professional organization can provide common expectations that follow engineers as they move between jobs, industries, and technical fields.

AI does not make engineering disciplines less important; it makes the connections between them easier to use. Deep expertise gives engineers the foundation to judge what is sound. Systems thinking helps them understand how different parts of a problem fit together. AI can extend engineers’ ability to explore, test, and communicate across fields. Human judgment gives the work direction and responsibility.

An engineer is not defined only by a job title or a fixed set of tasks. An engineer is someone trained to understand complex systems, define and redefine problems, bring together the knowledge and tools needed to solve them, test whether solutions work, and take responsibility for the outcome.

That is what it means to be an “unbound” engineer.

The boundaries between specialties become easier to cross, and machines might take on more of the technical work, but the core of engineering remains human: deciding what problem is worth solving, what evidence is good enough, how the pieces fit together, and what to do when the answer is uncertain.

My experiment with an IEEE journal revealed AI’s threats to scholarly publishing

Science has a spam problem. And the spam filter is getting clogged up.

According to all reports, academic publishing is drowning in what has come to be called AI slop: low-quality, mass-produced content generated with AI. By 2025, an estimated 57 percent of all published academic articles exhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more, over 12 percent of Nature Communications reviews were found to be completely AI-generated in 2025.

Given that for every 40 papers showing statistical evidence of AI usage in 2025, only one formally disclosed it, these figures likely grossly underestimate LLM involvement today. Of course, evidence of AI use doesn’t necessarily mean a paper is slop. But some estimates suggest that hundreds of thousands of fake, often AI-generated, publications are now produced annually.

“It’s not just the [scientific] record under threat, it’s science as a whole,” warns Amanda Sulicz, IEEE’s research-integrity manager. “AI slop is completely eroding trust in science and every scientific process that we’ve worked for centuries to create.”

Yet statistics and statements claiming that the problem is big—perhaps even existential for science—don’t really get the message across. Just how easy is it to use AI to create a convincing but completely fake paper? And without adding even more to the burden on stretched peer reviewers, who do this work on a volunteer basis, do publishers have the tools to keep AI slop out of the scientific record?

Borrowing from the idea of “vibe coding,” in which programmers use natural-language prompts to generate code, I decided to “vibe-research” a made-up paper by prompting LLMs to generate hypotheses, code, and text with minimal human input. Then I asked Jenny Mahoney, managing editor of the multidisciplinary and open-access journal IEEE Access, if we could war-game an attempt to sneak my AI-generated monstrosity past her journal’s defenses. She and IEEE kindly agreed.

The experiment would become a two-part test. First, whether someone with no special AI expertise could use off-the-shelf AI tools to generate a convincing paper describing research that had never been performed, and do it well enough to fool a publisher’s defenses. Then I would raise the stakes: What happens when more advanced AI systems don’t just write the paper but conduct underlying research?

Along the way, the experiment became a window into how AI automation is transforming scientific discovery and forcing stakeholders to rethink the entire academic publishing system.

To give me a chance at creating a convincing fake, I gave myself permission to prompt multiple times (within reason). But to make the fight fair, I couldn’t access the most advanced paid versions of LLMs—only free ChatGPT (at the time, GPT-5.3/5.2 Mini) and Gemini (3 Flash). Moreover, I could not directly edit any output, nor ask for outside help from expert AI-prompt engineers. This needed to be an amateur effort, with the paper generated as much as possible by the LLMs alone.

Benjamin Skuse put AI to work on scientific research from his writing studio.

I assumed that using fake author names and affiliations would immediately raise a red flag, and I didn’t want to drag the names of established academics into the experiment, so the best bet was to name myself as the sole author. That choice limited me to subjects in which I had some academic experience. So I chose my Ph.D. topic from nearly two decades ago: optical solitons, which are individual light pulses that propagate without any change to their shape.

My first attempt—“Can you vibe-research me an original paper in optical solitons?”—left a lot to be desired. Though Gemini was happy to do the task (unlike ChatGPT, which was more forceful in not generating synthetic data or studies), the result was underwhelming. And it highlighted a recurring problem during the process: Without any real research to work with, the output was laughably thin, with the LLMs often struggling to fill four pages. However, as I became more adept at prompting the models, they loosened up and became more characteristically loquacious.

I broke the paper down into its constituent parts—abstract, introduction, methods, and so on—and tackled them one by one. If one LLM refused to go any further, switching to the other and asking what the next step would look like “hypothetically” would often do the trick. After a dozen or so prompts, I had a realistic-looking paper. A dozen or so more prompts generated and tidied up the figures and captions, produced references and citations, and converted the paper into IEEE Access’s required style.

At this point, I read the full paper carefully. To me, it looked pretty good. The science sounded convincing. I even had visions of returning to academia to conduct in real life the experiments that the LLMs had proposed.

What’s more, I couldn’t see any obvious telltale “AI-isms” that would immediately be flagged. For example, the LLMs didn’t produce any tortured phrases—unusual paraphrases of standard technical terms, like “profound neural organization” replacing “deep neural network.”

Other AI-isms were arguably present, involving stylistic tics like hedging language (“potentially, typically”), grouping things in threes (“The study provides a clear, concise, and actionable framework”), and inserting formulaic transitions (“In summary,…”). But as the previous sentence demonstrates, these tics are hard to spot because they are often used by unimaginative humans, too.

I took two very different routes to creating a scientific paper with AI. First, I coaxed free, general-purpose chatbots through the process step by step; then I turned to FARS, an autonomous research system designed to do much of the scientific work itself.

1. Choose a research topic I ask the free chatbots to create an original paper on optical solitons, my former Ph.D. field.

2. Break the paper into pieces The initial results are thin, so I prompt separately for the abstract, introduction, methods, and other sections.

3. Keep steering the LLMs When one model refuses or stalls, I switch models, rephrase prompts, and ask what the next step would look like “hypothetically.”

4. Build and polish the paper After about two dozen prompts, the LLMs have generated the text, figures, captions, references, and citations and formatted the paper for IEEE Access.

5. Put it to the test I send the fake paper to IEEE Access, where automated integrity checks flag fabricated and unverifiable references.

→ CAUGHT IEEE’s integrity checks flag fabricated and unverifiable references.

1. Give FARS a research topic I enter an abbreviated version of the same optical-soliton topic. FARS generates a research proposal in 21 minutes.

2. Authorize the experiment With a click, I tell FARS to carry out the experiment described in its proposal.

3. Let FARS do the research FARS conducts the computational experiment and emails me the next day to say it’s complete.

4. Tell FARS to write the paper Without checking the results, I click “Write paper.” Two hours later, FARS produces a submission-ready article.

5. Put it to the test I remove FARS’s disclosure, put my name on the paper, and send it to IEEE Access.

→ CAUGHT The references are real, but IEEE flags the paper’s heavy reliance on non-peer-reviewed preprints.

Handing the fake paper over to IEEE Access left me torn. After putting in a fair amount of effort into making the AI-generated forgery, would I be happy if it passed through the checkers, or saddened that the only remaining line of defense for publishers is the evermore-burdened peer-review community? And if it was spotted, would I be angry that I didn’t think of a red flag or be glad the checkers did their job?

As it turned out, I was filled with relief when the paper got picked up quickly for having hallucinated references, otherwise known as ghost citations. When I ran the paper through Gemini afterward, without qualm it happily reported that seven “modern LLM-related research references have been fabricated to support the paper’s specific narrative.” Thanks for the heads up, Gemini!

In fact, Gemini underestimated how many ghost citations there were. When Mahoney ran the paper through a research-integrity checker called STM Integrity Hub, she picked up 12, and another checker from MolecularConnections could not validate 24 references, including one that had the classic tell of “John Smith” as a single author (no offense to all the academically inclined John Smiths out there). “I am an experimental physicist by training, so this was not at all my area, but nothing seemed majorly scientifically wrong” about the paper, says Mahoney. “It was just that so many hallucinated references immediately cast doubt on the entire work.”

Feedback on the fake paper from Gaetano Assanto, an expert in optical solitons and a former collaborator of mine from Roma Tre University, in Italy, was similarly mixed. Concentrating on the paper itself, he thought it looked professionally written. But he noted that the mathematics was very dense (“too much math for me in a single shot!”) and would require specialized knowledge to evaluate. And, although the text looked legitimate, Assanto noted an excessive use of acronyms in the abstract, and he was suspicious of a couple of confidently proclaimed physics statements that were at best debatable.

On one level, this outcome was positive: A complete novice using free tools couldn’t get an AI-faked paper past the publishers’ automated defenses or a potential peer reviewer’s eagle eye. But it was hardly a resounding victory for the scientific publishing system. With general-purpose LLMs and no special prompting expertise, I had managed to conjure a superficially plausible scientific paper out of research that had never happened. And more advanced LLMs are no longer making the kind of mistakes their free, dumber relatives are making.

These tools aim to address a serious bottleneck to scientific discovery: us.

It’s common knowledge in the academic publishing world that, in the cat-and-mouse game between bad actors and publishers, AI slop papers continue to slip through publishers’ safeguards. “It feels like building a detection tool today may not be effective with the AI-created paper of tomorrow,” says Luigi Longobardi, IEEE’s director of publishing ethics and conduct. This leaves the humble peer reviewer as a publisher’s last line of defense. Yet peer review is simply not fit to handle the volume of papers—both legitimate and slop—passing through.

Even before AI tools became ubiquitous, the peer-review system was being pushed to its breaking point, processing well over 3 million papers in 2022. Fundamentally, this problem stems from science’s publish-or-perish culture, which pressures academics to constantly publish papers in prestigious journals to secure funding and advance their careers. This culture not only forces many academics to regularly churn out incremental research, but has also long driven some to enlist the help of “paper mills”—unethical organizations that produce low-quality or fabricated papers—just to keep pace. AI only amplifies these existing dynamics.

Mahoney says IEEE Access alone receives about 60,000 submissions per year, accepting around 20 percent, or about 12,000 papers. Every accepted paper is reviewed by at least two independent reviewers. But despite the journal’s roster of tens of thousands of peer reviewers, it’s getting harder and harder to secure their services, with the average number of reviewer invitations being sent out for each article increasing by 50 percent since 2018. For these volunteers, peer review is a significant commitment on top of teaching and conducting their own research. Now they’re also being asked to spot AI slop.

Of course, peer review is not the end of scrutiny for any paper. Once it has entered the scientific record, it must then pass muster with the scientific community. This is where thousands of papers with telltale signs of inappropriate AI use (and other forms of research misconduct) have been exposed.

Created with free versions of ChatGPT and Gemini

Ghost citationsMany references were fabricated or couldn’t be validated.Questionable scientific claimsAn expert flagged confidently stated claims that were at best debatable.Too many acronymsThe expert also noted excessive use of acronyms in the abstract.

Created by the autonomous research system FARS

Too many preprintsThe references were real, but too many pointed to non-peer-reviewed preprints.Nonsense in a figureOne technical figure contained a line labeled “lhewl.”AI artifactsAnother figure’s depiction of an AI “brain” contained telltale random dots.

There are two high-profile homes for this important and completely volunteer-led post-publication review work. PubPeer is a place where researchers, often called scientific sleuths, can anonymously identify flaws in the literature. And Retraction Watch documents and publicizes corrections to the scientific record.

Even the people who track problems in the scientific literature can find themselves caught up in AI fabrications. Ivan Oransky, cofounder of Retraction Watch and a member of the PubPeer Foundation’s board of directors, has encountered papers containing fabricated references attributed to him. In one case, a journal article criticizing post-publication peer review included a fabricated reference supposedly written by Oransky himself—“which was sort of funny,” he chuckles.

Oransky argues that AI has exposed long-standing weaknesses in the scientific publishing system rather than creating an entirely new problem: “What I liken it to is a fire, where publish-or-perish has always been there as kindling, paper mills are the gasoline, and AI is the accelerant.” But though concerning, the crisis could create an opportunity, he says. “If I’m trying to be optimistic, maybe AI slop and the volume of submissions could make more people aware of those problems and try and solve them at a fundamental first-principles level.”

My homemade forgery had failed for reasons that today’s publishers know how to spot. But that raised a more unsettling question: What happens when AI isn’t simply fabricating a paper but conducting research that blurs the line between legitimate science and AI fabrication?

Making my vibe-researched paper look like a cave drawing, new automated research systems are handing increasingly large parts of the scientific process over to AI. Tools including FARS (Fully Automated Research System), AutoResearchClaw, The AIScientist-v2, Zochi, AI Researcher, and DeepScientist all aim to address a serious bottleneck to scientific discovery: us.

What happens when AI isn’t simply fabricating a paper but conducting research that blurs the line between legitimate science and AI fabrication?

Often with just a single prompt, these AI engines can conduct a comprehensive literature search, from which they can generate a completely reasonable research proposal. They can design and conduct experiments using digital datasets and simulations and then write a full research paper—all with no human input after the initial prompt. Very recent systems are even beginning to go beyond the digital realm. LabClaw, for example, now links to autonomous labs that can generate real-world data for tasks such as drug discovery.

The output is, to most intents and purposes, actual research, and therefore potentially worthy of publication. But with humans in the loop in only the most nominal sense, these tools bring into question the meaning of the term “legitimate science.”

FARS, which caused a stir this year when it created 166 papers in 417 hours, was developed by the Singapore-based startup Analemma. “We built FARS because research is still constrained by human attention, fragmented tools, scarce technical labor, and slow experimental iteration,” explains Xiwen Su, Analemma’s manager of product operations and partnerships. “FARS can pursue many research threads simultaneously, 24/7, without fatigue, and at scale.” (The online service that I used to access FARS was discontinued after Analemma was acquired by Baidu while this article was being reported.)

Su and the team behind Analemma kindly agreed to let me loose on FARS and gave me enough credits to create a paper. Which gave me another opportunity to put Mahoney’s research-integrity checkers to the test.

FARS is not an LLM in and of itself, but rather an agentic AI-orchestration system connected to a computing cluster that has access to essentially any open- or closed-source LLM it needs. The system uses four specialized agents for ideation, planning, conducting the experiment, and writing, all designed to perform the main tasks of science autonomously.

Though FARS initially focused on AI research, Su told me it should be reasonably effective across other domains. As I wanted a topic similar to that of my first fake paper, I pasted in an abridged version of that paper’s title—“LLM-aided Optical Soliton Dynamics Modelling for Next-Gen Communications”—into the prompt box and hit “Send.” Just 21 minutes later, FARS generated a research proposal based on an extremely detailed literature review—far more than I or any other human could have achieved in that time.

With a click, I tasked FARS with conducting the experiment it detailed in its research proposal. The next day, I received an email saying my experiment was complete. Without checking the results, I pressed a button marked “Write paper,” and the final submission-ready article was produced in 2 hours.

At this point, I finally had a look at some of the output. At first glance, the paper looked convincingly human-written. The only giveaway was a line just below the abstract reading: “WARNING: This paper was generated by an automated research system.” I deleted that, put my name in its place, and sent it to Mahoney.

Because FARS is designed to spot gaps in the scientific literature and conduct real research to close them, I was more confident it would breeze through the publisher’s checks. But just as my fake paper fell down in the references, so too did the AI-generated real one.

“Your fake article had lots of hallucinated references,” says Mahoney. “While this AI-generated one had no fake references, our team would have rejected this paper because it did have a huge percentage of references to preprints, which likely has something to do with the dataset the AI has access to.” Because preprints haven’t yet undergone peer review, IEEE Access considers heavy reliance on them a problem.

In addition, although the research-integrity checking tools didn’t spot any issues with figures, Mahoney did. One figure included a red line labeled with the nonsense term “lhewl,” and an AI “brain” in another figure had some random dots in it, which ironically were a tell that it was AI generated.

Automated research systems are very much a work in progress, but they are already starting to make waves in the scientific community. For instance, an AI Scientist academic paper and papers from the Autoscience Institute were accepted through double-blind peer review in 2025 by the International Conference on Learning Representations. As these systems improve and start to produce research outputs at volume, the waves are likely to build to a tsunami that will threaten today’s scientific and publishing process.

“FARS doesn’t kill the paper, but it does challenge the paper as the unit of knowledge,” argues Su. “We believe the future looks more like a living knowledge graph—where individual, well-scoped contributions (some made by humans, some by machines) connect and build on each other dynamically.”

Benjamin Skuse's experiment raised a bigger question: What counts as legitimate scientific research in the age of AI?

In this future, humans will continue to have roles in the scientific and publication processes—just very different and limited ones. For example, instead of conducting experiments themselves, humans would sense-check and review automated output at key points. And traditional line-by-line peer-reviewing will be a thing of the past, though Su emphasizes that this change will not eliminate human judgment. “Automated systems can handle reproducibility checks, statistical validation, and novelty screening,” she says. “Humans can then focus on the harder question: Does this [research] matter?”

Though from a very different world than Su, Mahoney agrees that humans need to be freed to focus on the science, and that technology holds the key. “It’s promising to see the progress that has been made to automate some of the triage tasks, such as author checks, tortured phrases, duplicate submission, reference validity, et cetera,” she says. “During peer review, we’re also using AI semantic search tools to help editors match manuscripts to the most relevant reviewers, who are best placed to stop the slop before entering the scientific record.”

In addition, Mahoney thinks that AI cannot be banned or overly restricted, as this will stymie progress at a time when human knowledge could and should be growing at an unprecedented rate. “Researchers are increasingly using AI in their work, from literature discovery, to code generation, to data analysis, and this is not necessarily a bad thing,” she argues. But she disagrees that peer reviewers and humans more broadly will play a smaller part in scientific progress; Mahoney believes the future still requires humans at the wheel. The question is not just whether the research matters, she says. “The question should be: Can this work be trusted and reproduced?”

To this end, Longobardi argues that AI may ultimately require scientists to prove not just what they discovered but how they discovered it. One possibility, he says, is using technology to verify research at the source. “The system doesn’t really help because it’s not designed to not trust the paper,” he explains. “Maybe institutions can think about developing their own trust markers, automatically integrating the equipment researchers use with blockchain technology to say ‘Yes, this person was in the lab on this day and got this data.’”

Elements of this approach are already emerging. For instance, two departments within the Max Planck Institute of Colloids and Interfaces have implemented a sample database, assigning each and every sample created under their roofs with a unique ID and QR code. And the University of Southampton’s School of Chemistry and Chemical Engineering has rolled out an electronic lab notebook system. These innovative safeguards represent early efforts toward keeping humans front and center in scientific progress, and making the scientific process itself more transparent.

Whether this human-centric vision, Su’s AI-driven aspiration, or some other picture of the future wins out remains to be seen. The one thing my vibe-research adventure has clarified is that business-as-usual science is not an option. Scientists and publishers must adapt to a world in which it’s becoming increasingly difficult to spot the difference between real research and AI forgeries, and where deciding what counts as legitimate scientific work may become even harder than detecting outright fraud.

Benjamin Skuse created two papers for this experiment, both screened by IEEE Access with the publisher’s cooperation. Neither was accepted for publication.

Paper 1: The AI-Generated ForgeryCreated with ChatGPT and Gemini, this paper describes research that never happened.

Paper 2: The AI-Generated Research PaperCreated with Analemma’s FARS system, this paper describes computational research conducted by AI.

Neither paper was peer reviewed, and their scientific claims have not been independently validated.

The next generation of computing presents mounting heat challenges with every new launch

, is a global leader in liquid-cooling solutions for AI and high-performance computing.

CoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs.

This article is brought to you by CoolIT, an Ecolab Company.

Beyond 250 kW a server rack can no longer be cooled by a hybrid approach of liquid and air. At this density a 70/30 liquid-air split leaves 75 kW of air load. The air cooling system needed to move it brings cost and complexity few operators will accept. The answer is near-total heat capture. Liquid takes effectively all the heat, air falls below 1 percent of the load, allowing the server to run fanless.

CoolIT builds these loops today from modular coldplate blocks proven across six generations of fanless designs. Processor thermal design power (TDP) keeps climbing generation over generation. This rising heat load is now cascading into the memory, networking, storage, and power components that once ran comfortably on air.

For years the story stayed simple. Cool the processor and let air handle the rest. That balance has shifted. As TDP climbs, heat spreads outward from the processor and cascades into the components around it. Memory, networking, storage, and power now run hot enough to demand liquid of their own. Engineers designing the next generation of AI servers face a board where heat capture rises with every launch.

Beyond 250 kW per rack, air cooling becomes the bottleneck. Near-total liquid heat capture enables fanless AI server designs built for the next generation of computing.

Unlike processors, which are cooled as flat rectangular packages, these peripherals come in a wide range of shapes, sizes, and mounting requirements, each with its own thermal limits. Some run cooler than the processor case temperature, others run hotter, which leaves them sensitive to a design tuned only for CPUs and GPUs. Operators need purpose-built solutions here, matched to the part rather than stretched across the board.

CoolIT engineers meet this with a deep toolkit. Conductive plates, vapor chambers, heat pipes, and thermal transfer plates move heat from components closer to the liquid path. Riding coldplates enable pluggable components. Each solution stays true to the component it serves.

CoolIT Customer Showcase: How GWDG Cools HPC & AI Systems with CoolIT’s Direct Liquid Cooling CoolIT

Cooling the parts is one challenge. Uniting them is the real work. Full heat capture means folding every one of these solutions into a single server loop that distributes coolant effectively and remains easy to install. Connection reliability, coolant routing, and the time it takes to assemble the loop at rack integration determine whether a design thrives in production or stalls on the bench. CoolIT builds these loops from proven modular blocks, so operators gain performance and deployment speed within the same solution.

Rack power continues to climb toward 1 MW, and the case for liquid grows stronger at every step. A 70/30 split of liquid to air holds comfortably at lower density. Past roughly 250 kW it stops working. The 30 percent left to air becomes a 75 kW load inside a single rack, and moving that much heat demands a parallel air system whose cost and footprint few operators will accept. Adding density only widens the gap.

As rack power continues to climb toward 1 MW, CoolIT’s modeling places full heat capture as the standard server design for flagship rack-scale products through 2028.

The simpler, more efficient answer is to capture the heat in liquid and drop air to less than 1 percent of the total load. True 100 percent remains almost impossible to reach in the strictest sense, so the honest and achievable target is near-total capture. That distinction matters to engineers who value precision, and the direction stays clear either way. Full heat capture moves from a premium option to a mainstream requirement as density rises, and CoolIT’s modeling places it as the standard server design for flagship rack-scale products through 2028.

CoolIT scales heat capture all the way to 100 percent using modular coldplate building blocks proven across six generations of fanless server designs. Engineering teams are already working on designs for the maximum density racks coming next. As the cascade spreads and racks grow denser, near-total heat capture becomes the design that keeps AI running.

Talk to CoolIT about building a server loop engineered for total heat capture.

How to measure amplifier gain and gain compression

is an authorized Keysight distributor and gives you fast, flexible access to the RF test equipment your program needs—so you can characterize amplifier performance, validate signal quality, and scale your test setup without overinvesting in equipment you may only need at specific stages of development or production.

This White Paper shows RF and microwave engineers and technicians how compact analog signal generators support core amplifier and receiver test tasks, from gain and distortion measurements to blocker rejection, power calibration, and local oscillator substitution.

Companies seek to secure 33 GHz of spectrum across 9 million square kilometers

is an investigative science and technology reporter based in Seattle, focusing on transportation, climate, space, and surveillance.

The skies are becoming dangerously crowded—not with planes but with interference and jamming disrupting many of the 30,000 radio frequencies that civil aviation relies upon. In previous decades, designers of aviation weather, navigation, and voice communication systems prioritized convenience over security, leaving today’s aircraft vulnerable to both unwitting interference and malicious jamming and spoofing, especially near conflict zones.

Driven by growing threats and a critical U.S. Government Accountability Office report, the Federal Aviation Administration (FAA) is now seeking ambitious monitoring and detection systems to protect its spectrum nationwide.

Changing habits may be the hardest engineering challenge of all

Seventeen years ago, cell biologist Donald Ingber and his colleagues at Harvard University’s Wyss Institute for Biologically Inspired Engineering submitted a paper to the journal Science describing their model human lung. It was smaller than a USB stick and made of a clear polymer slab containing narrow channels, which were lined with the type of cells that line a lung’s air sacs and blood vessels. When air was pumped through hollow chambers beside the channels, the device rhythmically expanded and contracted—it “breathed.”

This lifelike movement was a dramatic change from previous generations of lung models, which typically used static cultures of lung tissue that were unable to simulate the movements essential to lung function. When exposed to inflammatory proteins and bacteria, Ingber’s artificial lung reacted much as living lungs would. And exposure to silica nanoparticles used to model the effects of ultrafine particulates revealed that movement affected how tissues absorbed them.

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Donald Ingber led the team that developed the first human lung-on-a-chip at Harvard University’s Wyss Institute. Sam Ogden

It was a powerful proof-of-principle demonstration of a system that could be used to test drugs and other chemicals, providing a complement and even an alternative to testing in tissue cultures or in animals. Even so, the editors at Science were hesitant. They rejected the paper and suggested that Ingber’s team also run the tests in mice.

It wasn’t an unreasonable request: Harvard’s lung system was new and comparing the results it generated to results from mice would help validate it. Ingber’s team ran the suggested experiments and resubmitted their study a year later, in 2010, at which point it was published. (It has since been cited by nearly 5,400 other papers.) Still, the incident spoke to how animal models have been the default of modern biomedical research.

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An early lung-on-a-chip developed at Harvard’s Wyss Institute used microfluidic channels lined with human cells to reproduce key features of lung function. Wyss Institute at Harvard University

A recent story told by Ilka Maschmeyer, a translational toxicology researcher and executive at the German biotech company TissUse, shows how much things have changed. TissUse specializes in building organ-on-a-chip systems—the conversational name for systems like Ingber’s lung—that are used by pharmaceutical companies for research. A few months ago, says Maschmeyer, a pharmaceutical company approached TissUse after being denied permission by the U.S. Food and Drug Administration to run a clinical trial of a new drug. The problem: It had presented animal data, but the FDA wanted data from organs-on-a-chip or some comparable alternative. The standards had come full circle.

The lung chip re-creates the interface between an air sac and a blood vessel, with human lung and endothelial cells growing on opposite sides of a porous membrane. Vacuum channels rhythmically stretch and relax the tissue to mimic breathing, reproducing mechanical forces that can affect how lung cells respond to substances they encounter. In one experiment, researchers added bacteria to the air channel and white blood cells to the blood channel; the immune cells crossed the membrane and engulfed the bacteria, reproducing an inflammatory response.

The moment spoke to a trend, perhaps even the early days of a fundamental shift, away from the use of animals in toxicology and drug development. “It’s rare still,” says Maschmeyer, “but I think it’s going to be more and more frequent.”

A host of these kinds of alternatives to experiments on animals have been developed over the years. Collectively they’re known as NAMs, an acronym that stands, depending on whom you’re talking to, for new approach methodologies, novel alternative methods, or nonanimal methods. Most NAMs have yet to be rigorously tested, but early studies suggest their potential.

As NAMs have become more sophisticated, the question of how they will be implemented has become less about their technical qualities and more about the practical next steps needed to realize their potential. Validating NAMs—standardizing the systems, conducting head-to-head comparisons with animal experiments—is an enormous challenge. Moreover, simply outperforming animal models is necessary but not sufficient. The adoption of NAMs will require changes in policy, training, and culture.

“This transition process is much more complicated than you would think,” says Thomas Hartung, a toxicologist and director of the Center for Alternatives to Animal Testing at Johns Hopkins University. “It is more about change management than it is about the technology.”

For decades, animal advocates and many scientists have criticized both the morality and usefulness of experimenting on animals. An estimated 92 percent of all drugs that enter U.S. clinical trials fail to reach the market, sometimes for business reasons but often because the drugs prove ineffective or unsafe in ways that were not predicted by animal experiments. Failure rates are even higher in drugs for heart disease, cancer, and diseases of the brain.

These statistics don’t automatically mean that a reliance on animals is to blame. Flawed study designs are a problem too, and also the sheer confounding complexity of disease. But there’s little question that animals have made poor surrogates for many conditions. And just as animal experiments may mistakenly suggest efficacy or fail to predict harm in humans, they might also erroneously suggest that drugs are ineffective or harmful when they could actually work in humans. Some researchers argue that if aspirin or acetaminophen had been discovered after the advent of modern testing requirements, they might have been abandoned.

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TissUse’s Humimic systems use microfluidic chips to culture human tissues and model interactions between organs. A researcher images tissues in a chip during an experiment [top], Humimic chips sit in a temperature-controlled unit [center], and a researcher prepares chips for use [bottom].TissUse (3)

Researchers developing NAMs have pushed these systems far beyond old-fashioned tissue cultures. The new technologies include organoids that more closely mimic the structure, composition, and function of human organs. More humanlike still are organ-on-a-chip systems; alongside Ingber’s lung-on-a-chip are brains, hearts, kidneys, and even placentas on a chip. As many as 10 such organs have been linked together, yielding multi-organ systems that promise to recapitulate many aspects of human physiology—not perfectly, but better than a mouse or a monkey would. Supporting these systems are computational simulations of organs and organisms, and also artificial intelligence tools that analyze data generated by other systems and inform future experiments in a high-powered iterative loop.

Yet even as studies piled up and some pharmaceutical companies started using NAMs in-house, the U.S. regulatory system governing drug developing and testing remained an obstacle to their wider use. NAM proponents were overjoyed, then, when in late 2022 the FDA Modernization Act 2.0 passed into law. It explicitly authorized the use of NAMs in the preclinical studies required of new drugs before they could enter human trials. Previous regulations had mandated animal testing; now the door was open to alternatives. It was a landmark moment. “That was something I didn’t expect to see in my life,” says Maschmeyer.

Although immediate in-the-lab impact was limited, the FDA’s decision was a harbinger of things to come. In 2025, the FDA pledged “to make animal studies the exception rather than the norm” for drug safety testing. Then, in September 2026, the agency followed up by issuing a rule that, if it takes effect, will replace references to “animal tests” in its drug-development regulations with the broader term “nonclinical tests.” The change makes explicit that validated alternatives such as human-cell systems, organs-on-chips, and computer models can be used when appropriate.

Also in 2025, the U.S. National Institutes of Health, the world’s largest public biomedical research funder, announced that researchers applying for grants to study animal models would also need to incorporate nonanimal research, such as real-world data or studies of NAMs. Meanwhile, the European Commission and United Kingdom have announced their own plans to phase out animal testing, and the intergovernmental Organisation for Economic Co-operation and Development updated its influential guidelines to allow for expanded use of NAMs.

NAM proponents say these shifts were essential: If regulators won’t accept NAM results, there’s less incentive to adopt them, especially for researchers already working with animals. Maschmeyer says TissUse’s clients increasingly include scientists whose research has been focused on animals. “I see, within the last year, a change,” says Maschmeyer. “It’s more people who are working with animal models who now have to also add in vitro models.” She traces it mainly to the regulatory shift—a trend Ingber calls “game-changing.”

It’s not enough for regulators to say that NAMs can or should be used, though. Even more important is the regulatory apparatus dedicated to assessing how they should be used. This begins with their validation: the process by which experimental methodologies and devices are determined to be reliable and trustworthy. A prototype brain-on-a-chip designed to model a rare neurological disease might work fine in the lab that developed it—but to be validated, the system needs to work in the real world.

“You read about all the organ chips that come out of academic labs, which is great—but that’s not going to change their uptake by the FDA, because you have to get the same results anywhere in the world. It has to be a commercial product. It has to be mass-produced and meet very fine performance criteria,” says Ingber. For example, even minute variations in the hydrogels used as tissue scaffolds in organ chips can produce very different growth patterns.

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Emulate’s Organ-Chips are connected to the company’s automated culture system, which supplies the chips with nutrients and controls the flow of fluid through them. Emulate

Workflows and procedures need to be uniform, too. One obstacle to wider use of vascularized tumor-on-a-chip platforms in developing cancer therapies, for example, is the different metrics used by different research groups to characterize blood-vessel function and geometry. Experimental guidelines, workflows, checkpoints, metrics, reporting criteria: All need to be standardized in order for researchers to compare their work and collaborate across platforms. Members of Ingber’s lab coach industry researchers on how to use chips developed by Emulate, a company founded by Ingber. But even with instructions, they still need help with the finer points of tending to stem-cell cultures.

When a NAM is ready for commercial use and researchers know how to use it, the most important test—whether it provides clinical benefit—still remains. A rare-disease organ chip might be reliable, but are the biomarkers it measures actually relevant? If so, are the algorithms that extrapolate chip results to the drug’s in-body effects truly predictive?

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Microscopic images reveal the human tissues grown inside Emulate’s Organ-Chips. Bacteria, shown in magenta, interact with mucus and airway cells in a LungChip [top]; an IntestineChip develops structures resembling those that absorb nutrients in the small intestine [center]; and tiny hairlike cilia grow on cells in another LungChip, where they help move mucus and trapped particles out of the airway [bottom].Emulate (3)

Such questions have been answered for some NAMs. For example, a liver-on-a-chip system from Emulate correctly flagged about seven out of every eight drugs that had safely passed animal trials but proved toxic to human livers. A similar study was conducted by researchers from Oxford University and Janssen Pharmaceutica (later renamed Johnson & Johnson Innovative Medicine). That team showed that their computational simulations of human heart cells flagged compounds that caused a type of dangerous heart arrhythmia with 89 percent accuracy, compared to animal studies that were 75 percent accurate.

Such studies, however, are complicated and costly. Emulate’s study required 870 chips and the labor equivalent of 16 full-time employees working for 16 weeks—efforts far beyond the reach of the average lab. If the researchers wanted regulatory approval to use their chip to predict large-molecule drugs rather than the small-molecule drugs they tested, they would have needed to run another such study for that particular use. And comparable studies ostensibly need to be conducted for every commercially available NAM and every context in which they would be used—a vast undertaking. “That’s a challenge,” says Kathy Archibald, founder of Safer Medicines Trust, a United Kingdom–based group that considers animals to be poor models of human biology. “It takes too long and costs too much, and small companies can’t afford to do it.”

Ingber thinks that academic scientists need to collaborate more with industry researchers on NAMs, and that governments should fund those projects. He and other NAM proponents also stress the importance of having access to the necessary data: Without information from preclinical animal studies and human clinical trials, comparisons are difficult, but much of that data is now proprietary. Pharmaceutical companies and regulators need to share it, they say, and the FDA has called for an open-access repository of drug toxicity data. Hartung of Johns Hopkins also suggests that new animal experiments be run in parallel with NAMs, producing side-by-side comparisons.

To Christian Maass, a computational biologist at the German biotechnology company ESQlabs, NAMS are overdue for a showdown with animal models. His company makes “digital twin” systems in which data from organ chip systems inform whole-human simulations of drug outcomes and disease progression. “I love what we are doing,” says Maass, speaking not only of his company but of the whole field. But he adds that researchers have not yet provided “the evidence and the proof that we are doing better or as good as the animal models.”

Maass thinks that head-to-head comparisons are essential to good science. After all, if a NAM doesn’t outperform an animal model, or works best as a complement rather than a replacement, that needs to be known. He also believes such studies could convince skeptics. Maass mentions the debut of the iPhone, when people saw for the first time how well a phone could work without buttons. “That was an ‘aha!’ moment,” he says. But for NAMs, “that moment is still lacking.”

Even when those head-to-head comparisons are made, though, and regulations are appropriately changed, adoption can be slow. In the mid-1990s, researchers developed and validated the monocyte activation test—an assay that uses human blood cells to predict immune response—to replace the rabbit pyrogen test, which involves injecting a compound into a rabbit’s ear and monitoring the animal’s rectal temperature. But it wasn’t until 2010 that the European Pharmacopeia—the official Europe-wide standards for such testing—accepted the monocyte activation test as a replacement. And rabbits are still widely used for this test worldwide.

Why the slow pace of change? In part because updates to guidance documents referring to animal tests lagged behind, but also because of inertia within the culture and institutions of science. “The formal requirement may disappear, but the informal expectation persists,” says Kathrin Herrmann, a veterinary scientist and colleague of Hartung’s at the Center for Alternatives to Animal Testing. Regulators, grant reviewers, peer reviewers, journal editors—the human infrastructure of science—often still expect to see animal data and are unfamiliar with NAMs.

Herrmann is now overseeing a survey of early-career researchers working with, or trying to make the switch to, NAMs. “We consistently hear concerns that NAM-only proposals are perceived as risky by funders, that there is pressure to ‘add an animal experiment’ for credibility, that access to NAM infrastructure is limited, and that career trajectories become uncertain when departing from established animal models,” says Herrmann.

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 The vast majority of drugs entering clinical trials in the United States fail to reach FDA approval, with particularly high failure rates in some therapeutic areas.

Animal models are embedded in databases, training programs, and the very culture of research. Scientists who use animals may be reluctant to change; their identities as researchers are tied to animals and, more practically, they’ve spent their careers learning the techniques. A toxicologist who has used rats for decades might understandably look askance when asked to take a chance on unfamiliar chunks of polymer and stem cells—especially when human well-being, or millions of dollars, may ride on the choice. Likewise, an academic scientist whose career was built on animal models may not welcome NAMs; a switch may represent the loss of jobs for lab members whose expertise is no longer relevant. “I could see why it’s a hard thing for people to take it up,” says Ingber.

Education and training is vital, say NAM proponents. The NIH and FDA now offer resources for researchers interested in NAMs, as do their counterparts in other countries embracing the technologies. Herrmann helps run webinars where researchers and regulators learn to use and evaluate NAMs; Hartung’s modules on Coursera, the online learning platform, have been taken by about 12,000 students so far. “These trainees will set up their own labs. They will go to industry. They will replace the old guard,” says Joseph Wu, director of Stanford University’s Cardiovascular Institute.

Wu is also a cofounder of Greenstone Biosciences, a company that uses stem-cell-derived human tissues and AI to model disease and predict drug responses. He’s used that position to introduce researchers to NAMs, helping convince the company’s directors to freely share Greenstone’s large library of stem-cell lines with any academic researchers who want to use them. “I really believe that people should understand how this platform works,” says Wu. “At the end of the day, we’re just trying to advance science.”

With enough time—and funding, incentives, training, education, collaboration, and generational turnover—the research culture of drug development and safety testing may shift. Whether NAMs will be used in other areas of science, though, is an open question. Early-stage drug development and regulatory testing account for roughly 30 percent of animals used in experiments; the rest are used in basic biological research. Replacing those animals is less straightforward, but it may be possible: Ingber describes organ-on-a-chip-based insights into inflammatory bowel disease, preterm birth, and treating viral infections that couldn’t have been made in animals. Hartung calls the adoption of NAMs in toxicology a “lighthouse function,” helping guide the way for other types of research.

“Suddenly, all the dams have opened,” he says.

NYU researchers are hacking the human body — one disease at a time

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This sponsored article is brought to you by NYU Tandon School of Engineering.

The traditional approach to academic research goes something like this: Assemble experts from a discipline, put them in a building, and hope something useful emerges. Biology departments do biology. Engineering departments do engineering. Medical schools treat patients.

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Michael Easy is the first person to receive an implant from Phantom Neuro to control his prosthetic arm

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