384,000 sites pull code from sketchy code library recently bought by Chinese firm

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More than 384,000 websites are linking to a site that was caught last week performing a supply-chain attack that redirected visitors to malicious sites, researchers said.

For years, the JavaScript code, hosted at polyfill[.]com, was a legitimate open source project that allowed older browsers to handle advanced functions that weren’t natively supported. By linking to cdn.polyfill[.]io, websites could ensure that devices using legacy browsers could render content in newer formats. The free service was popular among websites because all they had to do was embed the link in their sites. The code hosted on the polyfill site did the rest.

The power of supply-chain attacks

In February, China-based company Funnull acquired the domain and the GitHub account that hosted the JavaScript code. On June 25, researchers from security firm Sansec reported that code hosted on the polyfill domain had been changed to redirect users to adult- and gambling-themed websites. The code was deliberately designed to mask the redirections by performing them only at certain times of the day and only against visitors who met specific criteria.

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Autonomous Vehicles Can Make All Cars More Efficient

Autonomous vehicles have been highly anticipated because of the possibility that they will greatly reduce or perhaps eliminate the collisions that cause more than one million deaths each year. But safety isn’t the only potential benefit self-driving cars can offer: Teams of researchers around the world are showing that autonomous vehicles can also drive more efficiently than humans can. A U.S. Department of Energy program called NEXTCAR (Next-Generation Energy Technologies for Connected and Automated On-Road Vehicles), for example, is betting that a mix of new smart vehicle technologies can boost fuel efficiency by as much as 30 percent.

As part of the NEXTCAR program, San Antonio, Texas-based Southwest Research Institute (SwRI) showcased advances in autonomous vehicle technology that will improve vehicles’ fuel economy—including the fuel efficiency of non-autonomous automobiles that just so happen to be in traffic with autonomous ones. The demonstration was held at the ARPA-E Energy Inovation Summit in Dallas in late May.

Making an Efficient Autonomous Vehicle

The SwRI team retrofitted a 2021 Honda Clarity hybrid with basic autonomous features such as perception and localization. On the day of the summit, they drove the vehicle along a route encircling the parking lot of the convention center where the summit was held. SWRI’s Ranger localization system, which the researchers installed on the Honda, has a downward-facing camera that captures images of the ground. By initially mapping the driving surface, Ranger can later localize the vehicle with centimeter-level accuracy, using the ground’s unique “fingerprint” combined with GPS data. This precision ensures the vehicle drives with exceptional control.

“It’s almost like riding on rails,” says Stas Gankov, a researcher in SwRI’s powertrain engineering group. For this project, his group collaborated with other divisions at the institute, such as the intelligence systems division, which developed the autonomy software stack added to the Honda Clarity.

Just as important, however, was the addition of an eco-driving module, a key innovation by SwRI. The eco-mode determines the most economical driving speed by considering various factors such as traffic lights and surrounding vehicles. This system employs predictive control algorithms to help solve a tricky optimization problem: How can cars minimize energy consumption while maintaining efficient traffic flow? SwRI’s eco-mode aims to reduce unnecessary acceleration and deceleration in order to optimize energy usage without impeding other vehicles.

“Autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them.” —Stas Gankov, Southwest Research Institute

To illustrate how the technology works, the team installed a traffic signal along the demonstration pathway. Gankov says an actual traffic light timer from a traffic signal cabinet was connected to a TV screen, providing a visual for attendees. A dedicated short range communications (DRSC) radio was also attached, broadcasting the signal’s phase and timing information to the vehicle. This setup enabled the vehicle to anticipate the traffic light’s actions far more accurately than a human driver could.

For instance, Gankov says, if the Honda Clarity was approaching a red light that was about to turn green, it would know the light was due to change and so avoid wasting energy by braking and then accelerating again. Conversely, if the car was approaching the signal as it was about to turn from green to yellow to red, the vehicle would release the accelerator and let friction slow it to a crawl, avoiding unnecessary acceleration in an attempt to beat the light.

These autonomous driving strategies can lead to significant energy savings, benefiting not just the autonomous vehicles themselves, but also the entire traffic ecosystem.

“In a regular traffic situation, autonomous vehicles operating in eco-mode influence the driving behavior of all the cars behind them,” says Gankov. “The result is that even vehicles with Level 0 autonomy use fuel more sparingly.”

The Grand Vehicle Energy Plan

SwRI has been a participant in the NEXTCAR initiative since 2017. The program’s initial phase involved 11 teams, including SwRI, Michigan Technological University, Ohio State University, and the University of California Berkeley. SwRI, in collaboration with the University of Michigan, focused on optimizing a Toyota Prius Prime, already known for its fuel efficiency, to achieve a 20 percent improvement in energy usage through optimization algorithms and wireless communicating with its surroundings. This was accomplished without modifying the Toyota’s powertrain or compromising its emissions. The team utilized power split optimization, balancing the use of the gas engine and battery propulsion system for maximum efficiency.

Building on the success of NEXTCAR’s first phase, the program entered its second phase in 2021, with just SwRI, UC Berkeley, Michigan Tech and Ohio State remaining. The focus of NEXTCAR 2 has been determining how much automation could further enhance energy efficiency. Gankov explains that while the first phase demonstrated a 20 percent energy efficiency improvement over a baseline 2016 or 2017 model year vehicle with no autonomous driving capabilities, through the addition of vehicle-to-everything connectivity alone, the second phase is exploring the potential for an additional 10 percent improvement by incorporating autonomous features.

Gankov says SwRI initially intended to partner with Honda for NEXTCAR’s second phase, but when contracting issues arose, the nonprofit proceeded independently. Utilizing an autonomy platform developed by SwRI’s intelligence systems division, the NEXTCAR team equipped the Honda Clarity with what amounted to Level 4 autonomy in a box. This autonomy system features a drive-by-wire system, allowing the vehicle to automatically adjust its speed and steering based on inputs from the autonomy software stack and the eco-driving module. This ensures the vehicle prioritizes safety while optimizing for energy efficiency.

Employing techniques like efficient highway merging were key strategies in their approach to making the most of each tank of fuel or battery charge. “For example, in heavy traffic on the highway, calculating the most optimal way to merge onto the highway without negatively affecting the energy efficiency of the vehicles already on the highway is crucial,” Gankov noted.

As NEXTCAR 2 enters its final year, the demonstration at the ARPA-E Summit served as a testament to the progress made in autonomous vehicle technology and its potential to dramatically improve energy efficiency in transportation.

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High Schooler Brings IEEE Mobile Disaster-Relief Tech to Campus

Unlike most people who encounter the IEEE-USA MOVE (Mobile Outreach VEhicle) emergency relief truck, Ananya Yanduru wasn’t a survivor of a natural disaster who needed to charge her cellphone or access the Internet. Instead, the 16-year-old got a guided tour of the truck on the grounds of her high school. She had requested MOVE visit Canyon Crest Academy, in San Diego, so she and her classmates could learn about the technology it houses.

The vehicle is equipped with satellite Internet access and IP phone service. MOVE can charge up to 100 cellphones simultaneously. It also has a mobile television for tracking storms, as well as radios for communications. A generator and three solar panels on the roof power the technology.

When it’s not deployed to help in disaster recovery, the vehicle stops at venues so its team can provide guided tours, educating people about ways technology helps during disasters.

Yanduru spotted the truck in June 2023 when it was parked at the San Diego Convention Center. She was there to accompany her father, an IEEE senior member, to a conference.

“I saw that the truck had traveled across the United States to help with hurricanes, be there for disaster relief, and work with the American Red Cross,” she says. “I thought that was a big deal.” MOVE’s volunteers often coordinate their disaster-relief efforts with the Red Cross.

Tours were over for the day, but that didn’t stop her. She was so determined to explore the vehicle that as soon as she got home she went to the MOVE website and requested a visit to her school. It showed up a few weeks later.

Yanduru was most interested in its communications system. She was impressed that the vehicle had its own Wi-Fi network, she says.

“I really liked how the IEEE-USA MOVE truck is able to establish such a strong communication system in a disaster area,” she says. “The radio engineering communication part really clicked with me.”

The vehicle was a big hit at her school, Yanduru says. More than 70 students and teachers toured it. Some of the students brought their family and friends.

Qualcomm’s devices inspired an interest in engineering

Yanduru is no stranger to engineering or technology. She comes from a family of engineers and is a member of her school’s radio engineering, coding, and 3D printing clubs.

Her father, electrical engineer Naveen Yanduru, is vice president and general manager of Renesas Electronics, in San Diego. Her mother, electrical engineer Arunasree Parsi, has worked as a computer-aided design engineer for Qualcomm and other semiconductor companies. Parsi is now president and CEO of Kaleidochip, also in San Diego.

“I really liked how the IEEE-USA MOVE truck is able to establish such a strong communication system in a disaster area.”

Yanduru says her mother sparked her passion for technology. When the girl was a youngster, the two visited the Qualcomm Museum, which displays the company’s modems, chips, tracking systems, and other products.

“I got interested in engineering from looking at those devices and seeing how engineering could be applied to so many different aspects of the world and used in so many fields,” she says.

Her parents support her interest in engineering because “it’s something that we can talk about,” she says. “I always feel open to discussing technology with them because they have so much knowledge in the field.”

Students and teachers from San Diego’s Canyon Crest Academy line up to tour the IEEE-USA MOVE truck during its stop at the high school.Ananya Yanduru

Participating in ham radio, 3D printing, and coding clubs

It’s no surprise Yanduru was interested in the MOVE’s communication system. She is a cofounder and copresident of her school’s radio engineering club, which has 10 members. It teaches students about topics they need to know to pass the amateur radio licensing test.

Yanduru is a licensed amateur radio operator. Her call sign is K06BAM.

“Getting a license sounds cool to a lot of high school students,” she says, “so as the founders, we thought the club would get more interest if we showed them an easy way to get their ham radio license.”

Now that most members have a license, they decided to participate in other activities. They first chose NASA’s Radio JOVE. The citizen science project provides kits for building a simple radio telescope to conduct scientific analysis of planets, the Milky Way, and Earth-based radio emissions. The findings are then shared with radio observatories via the Internet.

The club’s students plan to build their telescope during summer break, Yanduru says, adding that in the next school year they’ll conduct experiments about energy coming from Jupiter, then will send their results to NASA for analysis.

Yanduru also helped establish the school’s 3D printing club. She teaches club members how to print. The six members also help teachers repair the printers.

Another hobby of hers is writing code. She is secretary of the academy’s Girls Who Code club, which has about 20 members, not including the classmates they teach. The program aims to increase the number of women in the tech field by teaching coding.

She is sharing the knowledge she gains from the club as a volunteer teaching assistant for the League of Amazing Programmers. The San Diego–based nonprofit after-school program trains students in grades 5 to 12 on Java and Python.

“I really like being part of all the clubs,” she says, “because they use different aspects of engineering. For 3D, you really get to see the creative and the physical aspects. Radio is obviously more abstract. And coding is fun.”

Yanduru is still a few years away from attending college, but she says she plans to pursue an engineering degree. Choosing which field is a dilemma, she says.

“There’s a lot of things in electrical engineering and computer engineering that I find interesting,” she says. “I’ll definitely be studying something in one of those fields.”

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“RegreSSHion” vulnerability in OpenSSH gives attackers root on Linux

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Researchers have warned of a critical vulnerability affecting the OpenSSH networking utility that can be exploited to give attackers complete control of Linux and Unix servers with no authentication required.

The vulnerability, tracked as CVE-2024-6387, allows unauthenticated remote code execution with root system rights on Linux systems that are based on glibc, an open source implementation of the C standard library. The vulnerability is the result of a code regression introduced in 2020 that reintroduced CVE-2006-5051, a vulnerability that was fixed in 2006. With thousands if not millions of vulnerable servers populating the Internet, this latest vulnerability could pose a significant risk.

Complete system takeover

“This vulnerability, if exploited, could lead to full system compromise where an attacker can execute arbitrary code with the highest privileges, resulting in a complete system takeover, installation of malware, data manipulation, and the creation of backdoors for persistent access,” wrote Bharat Jogi, the senior director of threat research at Qualys, the security firm that discovered it. “It could facilitate network propagation, allowing attackers to use a compromised system as a foothold to traverse and exploit other vulnerable systems within the organization.”

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How to Build EV Motors Without Rare Earth Elements

The dilemma is easy to describe. Global efforts to combat climate change hinge on pivoting sharply away from fossil fuels. To do that will require electrifying transportation, primarily by shifting from vehicles with combustion engines to ones with electric drive trains. Such a massive shift will inevitably mean far greater use of electric traction motors, nearly all of which rely on magnets that contain rare earth elements, which cause substantial environmental degradation when their ores are extracted and then processed into industrially useful forms. And for automakers outside of China, there is an additional deterrent: Roughly 90 percent of processed rare earth elements now come from China, so for these companies, increasing dependence on rare earths means growing vulnerability in critical supply chains.

Against this backdrop, massive efforts are underway to design and test advanced electric-vehicle (EV) motors that do not use rare earth elements (or use relatively little of them). Government agencies, companies, and universities are working on this challenge, oftentimes in collaborative efforts, in virtually all industrialized countries. In the United States, these initiatives include long-standing efforts at the country’s national laboratories to develop permanent magnets and motor designs that do not use rare earth elements. Also, in a collaboration announced last November, General Motors and Stellantis are working with a startup company, Niron Magnetics, to develop EV motors based on Niron’s rare earth–free permanent magnet. Another automaker, Tesla, shocked observers in March of last year when a senior official declared that the company’s “next drive unit,” which would be based on a permanent magnet, would nevertheless use no “rare earth elements at all.” In Europe, a consortium called Passenger includes 20 partners from industry and academia working on rare earth–free permanent magnets for EVs.

We have been working for nearly a decade on magnetic and other aspects of traction-motor design at Oak Ridge National Laboratory (ORNL), in Tennessee, a hub of U.S. research on advanced motors for EVs. Along with colleagues from the National Renewable Energy Laboratory, Ames Laboratory, and the University of Wisconsin, Madison, we have been studying advanced motor concepts as part of the U.S. Department of Energy’s U.S. Drive Technologies Consortium. The group also includes Sandia National Laboratories, Purdue University, and the Illinois Institute of Technology.

With all of this activity, you would think that engineers would have by now developed a sophisticated understanding of what is possible with rare earth–free electric motors. And indeed they have. We and other researchers are evaluating promising permanent-magnet materials that don’t use rare earth elements, and we are evaluating possible motor-design changes required to best use these materials. We are also evaluating advanced motor designs that do not use permanent magnets at all. The bottom line is that replacing rare earth–based magnets with non–rare earth ones comes at a cost: degraded motor performance. But innovations in design, manufacturing, and materials will be able to offset—maybe even entirely—this gap in performance. Already, there are a few reports of tantalizing results with innovative new motors whose performance is said to be on a par with the best permanent-magnet synchronous motors.

Why rare earths make the most powerful electric motors

Rare earth elements (which people in our line of work often refer to as REEs) have unique properties that make them indispensable to many forms of modern technology. Some of these elements, such as neodymium, samarium, dysprosium, and terbium, can be combined with ferromagnetic elements such as iron and cobalt to produce crystals that are not only highly magnetic but also strongly resist demagnetization. The metric typically used to gauge these important qualities of a magnet is called the maximum energy product, measured in megagauss-oersteds (MGOe). The strongest and most commercially successful permanent magnets yet invented, neodymium iron boron, have energy products in the range of 30 to 55 MGOe.

For an electric motor based on permanent magnets, the stronger its magnets, the more efficient, compact, and lightweight the motor can be. So the highest-performing EV motors today all use neodymium iron boron magnets. Nevertheless, clever motor design can reduce the performance gap between motors based on rare earth permanent magnets and ones based on other types of magnets. To understand how, you need to know a little more about electric motors.

The most common type of traction motor in electric vehicles is the interior-mount permanent-magnet synchronous motor. Permanent magnets inside the rotor interact with a rotating magnetic field created by electromagnet windings in the stator, which surrounds the rotor.Oak Ridge National Laboratory

There are two basic types of electric motors: synchronous and induction. Most modern electric vehicles use a type of synchronous motor that has a rotor equipped with permanent magnets. Induction motors use only electromagnets and are therefore inherently rare earth–free. But they are not used today in most EV models because their performance is generally not on a par with permanent-magnet synchronous motors, although several R&D projects in the United States, Europe, and Asia are trying to improve induction motors.

The term “synchronous motors” refers to the fact that the rotor of the motor (the part that turns) rotates in synchrony with the changing magnetic fields produced by the stator (the part that remains stationary). In the rotor, permanent magnets are embedded in a circle around the structure. In the stator, also in a circular arrangement, electromagnets are pulsed with electricity one after another to set up a rotating magnetic field. This process causes the rotor magnets and stator magnets to attract and repel one another sequentially, producing rotation and torque.

Synchronous motors, too, fall into several categories. Two important types are surface-mount permanent-magnet synchronous motors and synchronous reluctance motors. In the former group, permanent magnets are mounted on the external surface of the rotor, and torque is produced because different parts of the stator and rotor either attract or repel. In a synchronous reluctance motor, on the other hand, the rotor doesn’t need to have permanent magnets at all. What makes the motor spin is a phenomenon called magnetic reluctance, which refers to how much a material opposes magnetic flux passing through it. Ferromagnetic materials have low values of reluctance and will tend to align themselves with strong magnetic fields. This phenomenon is exploited to cause a ferromagnetic rotor, in a reluctance motor, to spin. (Some reluctance motors also employ permanent magnets to assist that rotation.)

If a motor depends mainly on the interaction between the stator and rotor magnetic fields, it is called a permanent-magnet dominated motor. If on the other hand it depends on the torque produced by differences in reluctance, it is a permanent-magnet assisted motor. The combined use of both types of torque—that produced by the attraction and repulsion of permanent magnets and that produced by the tendency of magnetic lines of force to flow along a path of least reluctance—is the key strategy being used by engineers striving to achieve high performance in a motor that is less reliant on REE magnets.

Replacing REE-based magnets with non-REE ones comes at a cost: degraded motor performance. But innovations in motor design, manufacturing, and materials will be able to offset—maybe even entirely—this gap in performance.

The most common motor type at the moment combining the two kinds of torque is the interior-mount permanent-magnet motor, in which the permanent magnets embedded within the rotor add to the reluctance torque. Many commercial EV manufacturers, including GM, Tesla, and Toyota, now use this type of rotor design.

The design of the motors for the Toyota Prius underscores the effectiveness of this approach. In these motors, the magnet mass decreased significantly over a period of 13 years, from 1.2 kilograms in the 2004 Prius to about 0.5 kg in the 2017 Prius. Much the same occurred with the Chevrolet Bolt motor, which reduced the overall usage of magnet material by 30 percent compared with the motor in its predecessor, the Chevrolet Spark.

Wringing the most out of permanent magnets without rare earths

But what about getting rid of REEs entirely? Here, there are two possibilities: Use REE-free permanent magnets in a motor designed to make the most of them, or use a motor that dispenses with permanent magnets entirely, in favor of electromagnets.

To understand the suitability of a particular REE-free permanent magnet for use in a powerful traction motor, you have to consider a couple of additional characteristics of a permanent magnet: remanence and coercivity. To begin with, recall the metric used to compare the strength of different permanent-magnet materials: maximum energy product. These three parameters—maximum energy product, remanence, and coercivity—largely indicate how well a permanent-magnet material will perform in an electric motor.

Remanence indicates the amount of magnetic intensity, as measured by the density of the lines of force, left in a permanent magnet after the magnetic field that magnetized this magnet is withdrawn. Remanence is important because without it you wouldn’t have a permanent magnet. And the higher the remanence of the material, the stronger the forces of magnetic attraction and repulsion that create torque.

The coercivity of a permanent magnet is a measure of its ability to resist demagnetization. The higher the value of coercivity, the harder it is to demagnetize the magnet with an external magnetic field. For an EV traction motor, an optimal permanent magnet, such as neodymium iron boron, has high maximum energy product, high remanence, and high coercivity. No REE-free permanent magnet has all of these characteristics. So if you replace neodymium iron boron magnets with, say, ferrite magnets in a motor, you can expect a decrease in torque output and also must accept a greater risk that the magnets will demagnetize during operation.

An experimental motor built by the authors at Oak Ridge National Laboratory did not use any heavy rare earth elements. Neodymium iron boron permanent magnets are mounted on the external surface of the rotor. These magnets are represented by the teal-colored ring of blocks surrounding the copper-colored stator windings. To save space, the motor’s inverter and control electronics were installed inside the stator.Oak Ridge National Laboratory

Motor engineers can minimize the difference by designing a motor that exploits both permanent magnets and reluctance. But even with a highly optimized design, a motor based on ferrite magnets will be considerably heavier—perhaps a third or more—if it is to achieve the same performance as a motor with rare earth magnets.

One technique used to wring maximum performance out of ferrite magnets is to concentrate the flux from those magnets to the maximum extent possible. It’s analogous to passing moving water through a funnel: The water moves faster in the narrow opening. Researchers have built such machines, called spoke-ferrite magnet motors, but have found them to be about 30 percent heavier than comparable motors based on REE magnets. And there’s more bad news: Spoke-type motors can be complex to manufacture and pose mechanical challenges.

Some designers have proposed using another kind of non-REE magnet, one made from an aluminum nickel cobalt alloy called alnico, commonly used in the magnets that hold refrigerator doors shut. Although alnico magnets have high remanence, their coercivity is quite low, making them prone to demagnetization.

To address this issue, several researchers have studied and designed variable-flux memory motors, which use a magnetizing component of current to aid in torque production, in effect keeping the magnets from demagnetizing during operation. Additionally, researchers from the Ames Laboratory have shown that alnico magnets can have increased coercivity while maintaining their high remanence.

Three parameters—maximum energy product, remanence, and coercivity—largely indicate how a permanent magnet material will perform in an electric motor.

Lately, there’s been a lot of attention focused on a new type of permanent-magnet material, iron nitride (FeN). This magnet, produced by Niron Magnetics, has high remanence, equivalent to that of REE-magnets, but like alnico has low coercivity— about a fifth of a comparable neodymium iron boron magnet. Because of these fundamentally different properties, FeN magnets require the development of new rotor designs, which will probably resemble those of past alnico motors. Niron is now developing such designs with automotive partners, including General Motors.

Yet another REE-free permanent-magnet material that comes up in discussions of future motors is manganese bismuth (MnBi), which has been the subject of collaborative research at the University of Pittsburgh, Iowa State University, and Powdermet Inc. Together these engineers designed a surface-mount permanent-magnet synchronous motor using MnBi magnets. The remanence and coercivity of these magnets is higher than ferrite magnets but lower than neodymium iron boron (NdFeB). The researchers found that a MnBi-magnet motor can produce the same torque output as a NdFeB-magnet motor but with substantial compromises: a whopping 60 percent increase in volume and a 65 percent increase in weight. On the bright side, the researchers suggested that replacing NdFeB magnets with MnBi magnets could reduce the overall cost of the motor by 32 percent.

Another strategy for reducing rare earth content in motors involves eliminating just the heavy rare earth elements used in some of these magnets. NdFeB magnets, for example, typically contain small amounts of the heavy rare earth element dysprosium, used to increase their coercivity at high temperatures. (Heavy rare earth metals are generally in shorter supply than the light rare earths, such as neodymium.) The rub with not using them is that high-temperature coercivity then suffers.

So the major challenge in designing this kind of motor is keeping the rotor cool. Last year, at Oak Ridge National Laboratory, we developed a 100-kilowatt traction motor that uses no heavy rare earth elements in its magnets. Another nice feature is that its power electronics are integrated inside of it. These power electronics included the inverter, which takes direct-current power from the battery and feeds the motor with alternating current at the proper frequency to drive the machine.

We faced several fundamental challenges in keeping the magnets from getting too hot. You see, permanent magnets are good conductors. And when an electrical conductor moves in a magnetic field, which is what rotor magnets do while the motor is operating, currents are induced in it. These currents, which do not contribute to the torque, heat up the magnets and can demagnetize them. One way to reduce this heating is to break up the path of the circulating currents by making the magnets from thin segments that are electrically insulated from one another. In our motor, each of these segments was only 1 millimeter thick.

We chose to use a grade of NdFeB magnets called N50 that can operate at temperatures up to 80 °C. Also, we needed to use a carbon-fiber-and-epoxy system to reinforce the outer diameter of the rotor to let it spin at speeds as high as 20,000 rpm. After analyzing our motor prototype, we discovered it would be necessary to force air through the motor to reduce its temperature when operating at maximum speed. While that’s not ideal, it’s a reasonable compromise to avoid having to use heavy REEs in the design.

New approaches for advanced motors

Perhaps the most attractive near-term option to make powerful motors that lack REEs entirely is to build synchronous motors that have rotors equipped with electromagnets (meaning coils of wire), either with or without ferrite magnets included with them. But doing that requires that you somehow pass electrical current to those spinning coils.

The traditional solution is to use carbon brushes to make electrical contact with spinning metal rings, called slip rings. This technique allows you to apply direct current to the rotor to energize its electromagnets. Those brushes produce dust, though, and eventually wear out, so these motors aren’t suitable for use in EVs.

To address this issue, engineers have devised what are called rotary transformers or exciters. They employ an inductive or capacitive system to transfer power wirelessly to the spinning rotor. These motors have a great advantage over conventional, permanent-magnet synchronous motors, which is that their rotor’s magnetic field can be precisely adjusted, simply by controlling the current to the rotor’s electromagnets. That in turn permits a technique called field weakening, which allows high efficiency to be maintained through a wide range of operating speeds.

In the way they produce torque, synchronous electric motor types can be thought of as existing on a continuum between two different extremes. At the upper left in this chart is the surface permanent-magnet motor, which produces torque solely from the interaction between permanent magnets in the rotor and electromagnets in the stator. At the lower right is the synchronous reluctance motor, which creates torque by exploiting an entirely different phenomenon—magnetic reluctance, which refers to how much a material opposes magnetic flux passing through it. Most motor designs maximize torque by combining these two kinds of torque.Oak Ridge National Laboratory

A notable recent example is a motor built by the automotive supplier ZF Group. Last year the company announced it had produced a synchronous motor in which electromagnets in the rotor are powered by an inductive system that fits inside the machine’s rotor shaft. The 220-kW motor has power-density and efficiency characteristics on a par with those of the NdFeB permanent-magnet motors now used in EVs, according to a company official.

New materials can also help bridge the gap between REE-magnet and non-REE-magnet motors. For example, high-silicon steel, renowned for its superior magnetic properties, emerges as a promising candidate for rotor construction, offering the potential to improve the magnetic efficiency of REE-free motors. Concurrently, using high-conductivity copper alloys or ultraconducting copper strands can greatly reduce electrical losses and improve overall performance. Doubling the conductivity of copper, for example, could reduce the volume of certain motors by 30 percent. The strategic integration of such materials could dramatically narrow the performance gap between REE-containing and REE-free motors.

Another good example of an advanced material that could make a big difference is a dual-phase magnetic material developed by GE Aerospace, which can be magnetized either very strongly or not at all in specified areas. By selectively making certain sections of the rotor nonmagnetic, the GE Aerospace team demonstrated that it is possible to eliminate virtually all magnetic leakage, which in turn allowed them to forgo using rare earth permanent magnets in the motor.

How engineers will navigate the transition to REE-free motors

The transition toward rare earth–free motors for EVs is a major and pivotal engineering endeavor. It will be difficult, but research is beginning to yield intriguing and encouraging results. There will soon be multiple designs available—with, alas, a complex array of trade-offs. Motor weight, power density, cost, manufacturability, and overall performance dynamics will all be important considerations. And success in the marketplace will no doubt depend on an equally complex set of economic factors, so it’s very hard to predict which designs will dominate.

What’s becoming clear, though, is that it’s perfectly feasible that REE-free motors will one day become mainstream. That outcome will require continued and concerted effort. But we see no reason why engineers can’t navigate the complexities of this transition, ensuring that the next generation of EVs is more environmentally benign. Already, at ORNL and elsewhere, AI-enabled motor-design tools are accelerating the development of these REE-free motors.

Today, the large-scale use of REE magnets is marked by arguments pitting technological benefits against environmental and ethical considerations. Soon, those arguments could be much less relevant.

We’re not there yet. As with any major technological transition, the journey to rare earth–free motors won’t be short or straight. But it will be a journey well worth taking.

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3 million iOS and macOS apps were exposed to potent supply-chain attacks

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Vulnerabilities that went undetected for a decade left thousands of macOS and iOS apps susceptible to supply-chain attacks. Hackers could have added malicious code compromising the security of millions or billions of people who installed them, researchers said Monday.

The vulnerabilities, which were fixed last October, resided in a “trunk” server used to manage CocoaPods, a repository for open source Swift and Objective-C projects that roughly 3 million macOS and iOS apps depend on. When developers make changes to one of their “pods”—CocoaPods lingo for individual code packages—dependent apps typically incorporate them automatically through app updates, typically with no interaction required by end users.

Code injection vulnerabilities

“Many applications can access a user’s most sensitive information: credit card details, medical records, private materials, and more,” wrote researchers from EVA Information Security, the firm that discovered the vulnerability. “Injecting code into these applications could enable attackers to access this information for almost any malicious purpose imaginable—ransomware, fraud, blackmail, corporate espionage… In the process, it could expose companies to major legal liabilities and reputational risk.”

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The Best Bionic Leg Yet

For the first time, a small group of patients with amputations below the knee were able to control the movements of their prosthetic legs through neural signals—rather than relying on programmed cycles for all or part of a motion—and resume walking with a natural gait. The achievement required a specialized amputation surgery combined with a non-invasive surface electrode connection to a robotic prosthetic lower leg. A study describing the technologies was published today in the journal Nature Medicine.

“What happens then is quite miraculous. The patients that have this neural interface are able to walk at normal speeds; and up and down steps and slopes; and maneuver obstacles really without thinking about it. It’s natural. It’s involuntary,” said co-author Hugh Herr, who develops bionic prosthetics at the MIT Media Lab. “Even though their limb is made of titanium and silicone—all these various electromechanical components—the limb feels natural and it moves naturally, even without conscious thought.”

The approach relies on surgery at the amputation site to create what the researchers call an agonist-antagonist myoneural Interface, or AMI. The procedure involves connecting pairs of muscles (in the case of below-the-knee amputation, two pairs), as well as the introduction of proprietary synthetic elements.

The interface creates a two-way connection between body and machine. Muscle-sensing electrodes send signals to a small computer in the prosthetic limb that interprets them as angles and forces for joints at the ankle and ball of the foot. It also sends information back about the position of the artificial leg, restoring a sense of where the limb is in space, also known as proprioception.

Video 1 www.youtube.com

“The particular mode of control is far beyond what anybody else has come up with,” said Daniel Ferris, a neuromechanical engineer at the University of Florida; Ferris was not involved in the study, but has worked on neural interfaces for controlling lower limb prostheses. “It’s a really novel idea that they’ve built on over the last eight years that’s showing really positive outcomes for better bionic lower legs.” The latest publication is notable for a larger participant pool than previous studies, with seven treatment patients and seven control patients with amputations and typical prosthetic legs.

To test the bionic legs, patients were asked to walk on level ground at different speeds; up and down slopes and stairs; and to maneuver around obstacles. The AMI users had a more natural gait, more closely resembling movement by someone using a natural limb. More naturalistic motion can improve freedom of movement, particularly over challenging terrain, but in other studies researchers have also noted reduced energetic costs, reduced stress on the body, and even social benefits for some amputees.

Co-author Hyungeun Song, a postdoctoral researcher at MIT, says the group was surprised by the efficiency of the bionic setup. The prosthetic interface sent just 18 percent of the typical amount of information that’s sent from a limb to the spine, yet it was enough to allow patients to walk with what was considered a normal gait.

Next Steps for the Bionic Leg

AMI amputations have now become the standard at Brigham and Women’s Hospital in Massachusetts, where co-author Matthew Carty works. And because of patient benefits in terms of pain and ease of using even passive (or non-robotic) prosthetics, this technique—or something similar—could spread well beyond the current research setting. To date, roughly 60 people worldwide have received AMI surgery above or below either an elbow or knee.

In principle, Herr said, someone with a previously amputated limb, such as himself, could undergo AMI rehabilitation, and he is strongly considering the procedure. More than 2 million Americans are currently living with a lost limb, according to the Amputee Coalition, and nearly 200,000 lower legs are amputated each year in the United States.

On the robotics side, there are already commercial leg prosthetics that could be made compatible with the neural interface. The area in greatest need of development is the connection between amputation site and prosthesis. Herr says commercialization of that interface might be around five years away.

Herr says his long-term goal is neural integration and embodiment: the sense that a prosthetic is part of the body, rather than a tool. The new study “is a critical step forward—pun intended.”

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Shipt’s Algorithm Squeezed Gig Workers. They Fought Back

In early 2020, gig workers for the app-based delivery company Shipt noticed something strange about their paychecks. The company, which had been acquired by Target in 2017 for US $550 million, offered same-day delivery from local stores. Those deliveries were made by Shipt workers, who shopped for the items and drove them to customers’ doorsteps. Business was booming at the start of the pandemic, as the COVID-19 lockdowns kept people in their homes, and yet workers found that their paychecks had become…unpredictable. They were doing the same work they’d always done, yet their paychecks were often less than they expected. And they didn’t know why.

On Facebook and Reddit, workers compared notes. Previously, they’d known what to expect from their pay because Shipt had a formula: It gave workers a base pay of $5 per delivery plus 7.5 percent of the total amount of the customer’s order through the app. That formula allowed workers to look at order amounts and choose jobs that were worth their time. But Shipt had changed the payment rules without alerting workers. When the company finally issued a press release about the change, it revealed only that the new pay algorithm paid workers based on “effort,” which included factors like the order amount, the estimated amount of time required for shopping, and the mileage driven.

The Shopper Transparency Tool used optical character recognition to parse workers’ screenshots and find the relevant information (A). The data from each worker was stored and analyzed (B), and workers could interact with the tool by sending various commands to learn more about their pay (C). Dana Calacci

The company claimed this new approach was fairer to workers and that it better matched the pay to the labor required for an order. Many workers, however, just saw their paychecks dwindling. And since Shipt didn’t release detailed information about the algorithm, it was essentially a black box that the workers couldn’t see inside.

The workers could have quietly accepted their fate, or sought employment elsewhere. Instead, they banded together, gathering data and forming partnerships with researchers and organizations to help them make sense of their pay data. I’m a data scientist; I was drawn into the campaign in the summer of 2020, and I proceeded to build an SMS-based tool—the Shopper Transparency Calculator—to collect and analyze the data. With the help of that tool, the organized workers and their supporters essentially audited the algorithm and found that it had given 40 percent of workers substantial pay cuts. The workers showed that it’s possible to fight back against the opaque authority of algorithms, creating transparency despite a corporation’s wishes.

How We Built a Tool to Audit Shipt

It started with a Shipt worker named Willy Solis, who noticed that many of his fellow workers were posting in the online forums about their unpredictable pay. He wanted to understand how the pay algorithm had changed, and he figured that the first step was documentation. At that time, every worker hired by Shipt was added to a Facebook group called the Shipt List, which was administered by the company. Solis posted messages there inviting people to join a different, worker-run Facebook group. Through that second group, he asked workers to send him screenshots showing their pay receipts from different months. He manually entered all the information into a spreadsheet, hoping that he’d see patterns and thinking that maybe he’d go to the media with the story. But he was getting thousands of screenshots, and it was taking a huge amount of time just to update the spreadsheet.

That’s when Solis contacted Coworker, a nonprofit organization that supports worker advocacy by helping with petitions, data analysis, and campaigns. Drew Ambrogi, then Coworker’s director of digital campaigns, introduced Solis to me. I was working on my Ph.D. at the MIT Media Lab, but feeling somewhat disillusioned about it. That’s because my research had focused on gathering data from communities for analysis, but without any community involvement. I saw the Shipt case as a way to work with a community and help its members control and leverage their own data. I’d been reading about the experiences of delivery gig workers during the pandemic, who were suddenly considered essential workers but whose working conditions had only gotten worse. When Ambrogi told me that Solis had been collecting data about Shipt workers’ pay but didn’t know what to do with it, I saw a way to be useful.

Throughout the worker protests, Shipt said only that it had updated its pay algorithm to better match payments to the labor required for jobs; it wouldn’t provide detailed information about the new algorithm. Its corporate photographs present idealized versions of happy Shipt shoppers. Shipt

Companies whose business models rely on gig workers have an interest in keeping their algorithms opaque. This “information asymmetry” helps companies better control their workforces—they set the terms without divulging details, and workers’ only choice is whether or not to accept those terms. The companies can, for example, vary pay structures from week to week, experimenting to find out, essentially, how little they can pay and still have workers accept the jobs. There’s no technical reason why these algorithms need to be black boxes; the real reason is to maintain the power structure.

For Shipt workers, gathering data was a way to gain leverage. Solis had started a community-driven research project that was collecting good data, but in an inefficient way. I wanted to automate his data collection so he could do it faster and at a larger scale. At first, I thought we’d create a website where workers could upload their data. But Solis explained that we needed to build a system that workers could easily access with just their phones, and he argued that a system based on text messages would be the most reliable way to engage workers.

Based on that input, I created a textbot: Any Shipt worker could send screenshots of their pay receipts to the textbot and get automated responses with information about their situation. I coded the textbot in simple Python script and ran it on my home server; we used a service called Twilio to send and receive the texts. The system used optical character recognition—the same technology that lets you search for a word in a PDF file—to parse the image of the screenshot and pull out the relevant information. It collected details about the worker’s pay from Shipt, any tip from the customer, and the time, date, and location of the job, and it put everything in a Google spreadsheet. The character-recognition system was fragile, because I’d coded it to look for specific pieces of information in certain places on the screenshot. A few months into the project, when Shipt did an update and the workers’ pay receipts suddenly looked different, we had to scramble to update our system.

In addition to fair pay, workers also want transparency and agency.

Each person who sent in screenshots had a unique ID tied to their phone number, but the only demographic information we collected was the worker’s metro area. From a research perspective, it would have been interesting to see if pay rates had any connection to other demographics, like age, race, or gender, but we wanted to assure workers of their anonymity, so they wouldn’t worry about Shipt firing them just because they had participated in the project. Sharing data about their work was technically against the company’s terms of service; astoundingly, workers—including gig workers who are classified as “independent contractors”— often don’t have rights to their own data.

Once the system was ready, Solis and his allies spread the word via a mailing list and workers’ groups on Facebook and WhatsApp. They called the tool the Shopper Transparency Calculator and urged people to send in screenshots. Once an individual had sent in 10 screenshots, they would get a message with an initial analysis of their particular situation: The tool determined whether the person was getting paid under the new algorithm, and if so, it stated how much more or less money they’d have earned if Shipt hadn’t changed its pay system. A worker could also request information about how much of their income came from tips and how much other shoppers in their metro area were earning.

How the Shipt Pay Algorithm Shortchanged Workers

By October of 2020, we had received more than 5,600 screenshots from more than 200 workers, and we paused our data collection to crunch the numbers. For the shoppers who were being paid under the new algorithm, we found that 40 percent of workers were earning more than 10 percent less than they would have under the old algorithm. What’s more, looking at data from all geographic regions, we found that about one-third of workers were earning less than their state’s minimum wage.

It wasn’t a clear case of wage theft, because 60 percent of workers were making about the same or slightly more under the new scheme. But we felt that it was important to shine a light on those 40 percent of workers who had gotten an unannounced pay cut through a black box transition.

In addition to fair pay, workers also want transparency and agency. This project highlighted how much effort and infrastructure it took for Shipt workers to get that transparency: It took a motivated worker, a research project, a data scientist, and custom software to reveal basic information about these workers’ conditions. In a fairer world where workers have basic data rights and regulations require companies to disclose information about the AI systems they use in the workplace, this transparency would be available to workers by default.

Our research didn’t determine how the new algorithm arrived at its payment amounts. But a July 2020 blog post from Shipt’s technical team talked about the data the company possessed about the size of the stores it worked with and their calculations for how long it would take a shopper to walk through the space. Our best guess was that Shipt’s new pay algorithm estimated the amount of time it would take for a worker to complete an order (including both time spent finding items in the store and driving time) and then tried to pay them $15 per hour. It seemed likely that the workers who received a pay cut took more time than the algorithm’s prediction.

Shipt workers protested in front of the headquarters of Target (which owns Shipt) in October 2020. They demanded the company’s return to a pay algorithm that paid workers based on a simple and transparent formula. The SHIpT List

Solis and his allies used the results to get media attention as they organized strikes, boycotts, and a protest at Shipt headquarters in Birmingham, Ala., and Target’s headquarters in Minneapolis. They asked for a meeting with Shipt executives, but they never got a direct response from the company. Its statements to the media were maddeningly vague, saying only that the new payment algorithm compensated workers based on the effort required for a job, and implying that workers had the upper hand because they could “choose whether or not they want to accept an order.”

Did the protests and news coverage have an effect on worker conditions? We don’t know, and that’s disheartening. But our experiment served as an example for other gig workers who want to use data to organize, and it raised awareness about the downsides of algorithmic management. What’s needed is wholesale changes to platforms’ business models.

An Algorithmically Managed Future?

Since 2020, there have been a few hopeful steps forward. The European Union recently came to an agreement about a rule aimed at improving the conditions of gig workers. The so-called Platform Workers Directive is considerably watered down from the original proposal, but it does ban platforms from collecting certain types of data about workers, such as biometric data and data about their emotional state. It also gives workers the right to information about how the platform algorithms make decisions and to have automated decisions reviewed and explained, with the platforms paying for the independent reviews. While many worker-rights advocates wish the rule went further, it’s still a good example of regulation that reins in the platforms’ opacity and gives workers back some dignity and agency.

Some debates over gig workers’ data rights have even made their way to courtrooms. For example, the Worker Info Exchange, in the United Kingdom, won a case against Uber in 2023 about its automated decisions to fire two drivers. The court ruled that the drivers had to be given information about the reasons for their dismissal so they could meaningfully challenge the robo-firings.

In the United States, New York City passed the country’s first minimum-wage law for gig workers, and last year the law survived a legal challenge from DoorDash, Uber, and Grubhub. Before the new law, the city had determined that its 60,000 delivery workers were earning about $7 per hour on average; the law raised the rate to about $20 per hour. But the law does nothing about the power imbalance in gig work—it doesn’t improve workers’ ability to determine their working conditions, gain access to information, reject surveillance, or dispute decisions.

Willy Solis spearheaded the effort to determine how Shipt had changed its pay algorithm by organizing his fellow Shipt workers to send in data about their pay—first directly to him, and later using a textbot.Willy Solis

Elsewhere in the world, gig workers are coming together to imagine alternatives. Some delivery workers have started worker-owned services and have joined together in an international federation called CoopCycle. When workers own the platforms, they can decide what data they want to collect and how they want to use it. In Indonesia, couriers have created “base camps” where they can recharge their phones, exchange information, and wait for their next order; some have even set up informal emergency response services and insurance-like systems that help couriers who have road accidents.

While the story of the Shipt workers’ revolt and audit doesn’t have a fairy-tale ending, I hope it’s still inspiring to other gig workers as well as shift workers whose hours are increasingly controlled by algorithms. Even if they want to know a little more about how the algorithms make their decisions, these workers often lack access to data and technical skills. But if they consider the questions they have about their working conditions, they may realize that they can collect useful data to answer those questions. And there are researchers and technologists who are interested in applying their technical skills to such projects.

Gig workers aren’t the only people who should be paying attention to algorithmic management. As artificial intelligence creeps into more sectors of our economy, white-collar workers find themselves subject to automated tools that define their workdays and judge their performance.

During the COVID-19 pandemic, when millions of professionals suddenly began working from home, some employers rolled out software that captured screenshots of their employees’ computers and algorithmically scored their productivity. It’s easy to imagine how the current boom in generative AI could build on these foundations: For example, large language models could digest every email and Slack message written by employees to provide managers with summaries of workers’ productivity, work habits, and emotions. These types of technologies not only pose harm to people’s dignity, autonomy, and job satisfaction, they also create information asymmetry that limits people’s ability to challenge or negotiate the terms of their work.

We can’t let it come to that. The battles that gig workers are fighting are the leading front in the larger war for workplace rights, which will affect all of us. The time to define the terms of our relationship with algorithms is right now.

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