Concrete is the most used material in the construction industry and dates to the Roman Empire. Engineers at the University of Pittsburgh are now reimagining its design for the 21st century.

New research introduces metamaterial concrete for the development of smart civil infrastructure systems. The paper, “Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure,” presents a new concept for lightweight and mechanically-tunable concrete systems that have integrated energy harvesting and sensing functionality.

“Modern society has been using concrete in construction for hundreds of years, following its original creation by the ancient Romans,” said Amir Alavi, assistant professor of civil and environmental engineering at Pitt, who is the corresponding author on the study. “Massive use of concrete in our infrastructure projects implies the need for developing a new generation of concrete materials that are more economical and environmentally sustainable, yet offer advanced functionalities. We believe that we can achieve all of these goals by introducing a metamaterial paradigm into the development of construction materials.”

Alavi and his team have previously developed self-aware metamaterials and explored their use in applications like smart implants.

This study introduces the use of metamaterials in the creation of concrete, making it possible for the material to be specifically designed for its purpose. Attributes like brittleness, flexibility and shapeability can be fine-tuned in the creation of the material, enabling builders to use less of the material without sacrificing strength or longevity.

“This project presents the first composite metamaterial concrete with super compressibility and energy harvesting capability,” said Alavi. “Such lightweight and mechanically tunable concrete systems can open a door to the use of concrete in various applications such as shock absorbing engineered materials at airports to help slow runaway planes or seismic base isolation systems.”

Not only that, but the material is capable of generating electricity. While it cannot produce enough electricity to send power to the electrical grid, the generated signal will be more than enough to power the roadside sensors. The electrical signals self-generated by the metamaterial concrete under mechanical excitations can also be used to monitor damage inside the concrete structure or to monitor earthquakes while reducing their impact on buildings.

Eventually, these smart structures may even power chips embedded inside roads to help self-driving cars navigate on highways when GPS signals are too weak or LIDAR is not working.

The material is composed of reinforced auxetic polymer lattices embedded in a conductive cement matrix. The composite structure induces contact-electrification between the layers when triggered mechanically. The conductive cement, which is enhanced with graphite powder, serves as the electrode in the system. Experimental studies show that the material can compress up to 15% under cyclic loading and produce 330 μW of power.

The research team is partnering with the Pennsylvania Department of Transportation (PennDOT) through the IRISE Consortium at Pitt to develop this metamaterial concrete for use on Pennsylvania roads.

The paper, “Multifunctional Nanogenerator-Integrated Metamaterial Concrete Systems for Smart Civil Infrastructure,” was published in Advanced Materials.

A recent study that examined the relationship between artificial intelligence (AI) and law enforcement underscores both the need for law enforcement agencies to be involved in the development of public policies regarding AI—such as regulations governing autonomous vehicles—and the need for law enforcement officers to better understand the limitations and ethical challenges of AI technologies.

“Law enforcement agencies have a crucial role to play in implementing public policies related to AI technologies,” says Veljko Dubljević, corresponding author of the study and an associate professor of science, technology and society at North Carolina State University.

“For example, officers will need to know how to proceed if they pull over a vehicle being driven autonomously for a traffic violation. For that matter, they will need to know how to pull over a vehicle being driven autonomously. Because of their role in maintaining public order, it’s important for law enforcement to have a seat at the table in crafting these policies.”

“In addition, there are a number of AI-powered technologies that are already in use by law enforcement agencies that are designed to help them prevent and respond to crime,” says Ronald Dempsey, first author of the study and a former graduate student at NC State. “These range from facial recognition technologies to technologies designed to detect gunshots and notify relevant law enforcement agencies.”

“However, our study suggests that many officers do not understand how these technologies work, which makes it difficult or impossible for them to appreciate the limitations and ethical risks of those technologies. And that can pose significant problems for both law enforcement and the public.”

For this study, the researchers conducted in-depth interviews with 20 law enforcement professionals who work in North Carolina. The interviews addressed a range of issues, including the values and qualities that the study participants felt were critical for law enforcement officers.

While there was no consensus across a majority of study participants, there were several characteristics that cropped up repeatedly as important qualities for a law enforcement professional, with integrity, honesty and empathy being cited most often.

“Understanding what law enforcement deems to be desirable characteristics in officers is valuable, because these characteristics can inform the development of responsible design guidelines for AI technologies that law enforcement will use,” Dempsey says.

“Design guidelines can be used to inform AI decision-making, and it is easier for end users to work with AI tools if the values guiding AI decisions are consistent—or at least not in conflict—with the values of the end users,” says Dubljević.

The researchers also asked study participants about their views on AI in general, as well as existing and emerging AI technologies.

“We found that study participants were not familiar with AI, or with the limitations of AI technologies,” says Jim Brunet, co-author of the study and director of NC State’s Public Safety Leadership Initiative. “This included AI technologies that participants had used on the job, such as facial recognition and gunshot detection technologies. However, study participants expressed support for these tools, which they felt were valuable for law enforcement.”

The study participants also expressed concern about the future of autonomous vehicles, and what challenges they may pose to the law enforcement community.

“However, study participants did say that they would welcome public use of autonomous vehicles if that would reduce car accidents,” says Dubljević. “Specifically, the participants welcomed the idea of spending less time responding to vehicle accidents, which would allow them to focus on addressing crime.”

“There are always dangers when law enforcement adopts technologies that were not developed with law enforcement in mind,” says Brunet. “This certainly applies to AI technologies such as facial recognition. As a result, it’s critical for law enforcement officials to have some training in the ethical dimensions surrounding the use of these AI technologies. For example, where a law enforcement agency chooses to deploy AI tools will affect which portions of the public are subject to additional scrutiny.”

“It’s also important to understand that AI tools are not foolproof,” says Dubljević. “AI is subject to limitations. And if law enforcement officials don’t understand those limitations, they may place more value on the AI than is warranted—which can pose ethical challenges in itself.”

The paper is published in the journal Applied Sciences.

Unmanned aerial vehicles (UAVs), also known as drones, can help humans to tackle a variety of real-world problems; for instance, assisting them during military operations and search and rescue missions, delivering packages or exploring environments that are difficult to access. Conventional UAV designs, however, can have some shortcomings that limit their use in particular settings.

For instance, some UAVs might be unable to land on uneven terrains or pass through particularly narrow gaps, while others might consume too much power or only operate for short amounts of time. This makes them difficult to apply to more complex missions that require reliably moving in changing or unfavorable landscapes.

Researchers at Zhejiang University have recently developed a new unmanned, wheeled and hybrid vehicle that can both roll on the ground and fly. This unique system, introduced in a paper pre-published on arXiv, is based on a unicycle design (i.e., a cycling vehicle with a single wheel) and a rotor-assisted turning mechanism.

“Roller-Quadrotor is a novel hybrid terrestrial and aerial quadrotor that combines the elevated maneuverability of the quadrotor with the lengthy endurance of the ground vehicle,” Zhi Zheng, Jin Wang and their colleagues wrote in their paper. “Flying is achieved through a quadrotor configuration, and four actuators providing thrust. Rolling is supported by unicycle-driven and rotor-assisted turning structure. During terrestrial locomotion, the vehicle needs to overcome rolling and turning resistance, thus saving energy compared to flight mode.”

The hybrid terrestrial and aerial vehicle created by Zheng, Wang and their colleagues is a so-called quadrotor, which is an aircraft based on rotary wings that can hover above ground and fly. As it is based on a unicycle structure, it can also move on the ground on various terrains and pass through narrow gaps.

“This work overcomes the challenging problems of general rotorcraft, reduces energy consumption and allows movement through special terrains, such as narrow gaps,” the researchers wrote in their paper. “It also solves the obstacle avoidance challenge faced by terrestrial robots by flying.”

In their paper, Zheng, Wang and their colleagues present their vehicle’s design along with a series of models and controllers that allow it to roll, fly, and seamlessly transition between these two modes of operation. They also outline the results of a series of experiments, where a prototype of their vehicle was tested in an environment monitored by cameras and motion capture sensors.

“We design the models and controllers for the vehicle,” Zheng, Wang and their colleagues wrote in their paper. “The experiment results show that it can switch between aerial and terrestrial locomotion, and be able to safely pass through a narrow gap half the size of its diameter. Besides, it is capable of rolling a distance approximately 3.8 times as much as flying or operating about 42.2 times as lengthy as flying.”

The hybrid vehicle presented in this recent paper could soon be tested and evaluated in a wider range of environments, to further validate its performance. Initial results gathered by Zheng, Wang and their colleagues suggest that the vehicle could eventually be used to tackle complex real-world missions that entail moving on tricky terrains, entering narrow passages and operating for longer periods of time.

In their next studies, the researchers plan to enhance their design further, for instance by improving the accuracy of the models they created and introducing more advanced control algorithms. This could in turn make the vehicle’s transition from its rolling to flying modes smoother, while also improving its navigation capabilities.

“We are also considering structural optimization and weight reduction, to further improve the energy consumption performance,” the researchers concluded in their paper. “Furthermore, we will use planning algorithms to enhance vehicle mobility.”

© 2023 Science X Network

The last gas-powered muscle car from Dodge isn’t leaving the road without some squeals, thunder and crazy-fast speed.

The 2023 Challenger SRT Demon 170 will deliver 1,025 horsepower from its 6.2-liter supercharged V-8, and the automaker says it will be the quickest production car made.

Stellantis says it can go from zero to 60 miles per hour (97 kilometers per hour) in a scary 1.66 seconds, making it faster than even electric supercars from Tesla and Lucid.

It’s what the performance brand from Stellantis is calling the last of the rumbling cars that for decades were a fixture of American culture on Saturday night cruises all over the country.

Stellantis will stop making gas versions of the Dodge Challenger and Charger and the Chrylser 300 big sedan by the end of this year, squeezed out by stricter government fuel-economy regulations and an accelerating shift to electric vehicles to fight climate change.

The Canadian factory that makes all three cars will be retooled to make electric versions of larger cars starting next year. Stellantis hasn’t said whether all three models will survive, but it did show off a Charger Daytona SRT electric concept muscle car back in August.

Tim Kuniskis, CEO of the Dodge brand and the unofficial spokesman for America’s gas-powered rubber-burners, said that, while he’ll miss the traditional muscle, he’s excited about making electric performance vehicles.

“It’s the end of an era, for sure,” he said Monday. “Electric products, they’re very fast. Muscle cars, one of the primary ingredients is to be a fast accelerating car. So I’ve automatically got the power. Now I’ve just got to figure out ways to bring all the other elements in of the excitement of the driving experience.”

Since last summer, Dodge has been rolling out powerful special-edition “Last Call” versions of its gas powered muscle cars, culminating with an event Monday night to show the Challenger Demon 170 at the Las Vegas Motor Speedway drag strip.

The new Challenger Demon, a descendant of a car that first went on sale in 1969, also produces 945 pound-feet of torque, or rotational force—so much power that the company had to strengthen the rear drive shaft and differential with aerospace-grade metals.

According to Stellantis, the car will be the first production vehicle to run a quarter-mile (0.40 kilometers) in under nine seconds—8.91 to be exact. To do that, it hits a speed of just over 151 mph (243 kilometers per hour). Horsepower and speed depends on how much ethanol is in the fuel.

It gets only 13 miles per gallon in the city and 21 on the highway, but it’s doubtful anyone buying one will care even as the world deals with climate change.

Kuniskis says it’s a relatively small number of cars, and he says the ethanol they burn is cleaner than gasoline. Dodge, he said, will have built 2 million muscle cars by the time production of gas versions ends Dec. 31. Dodge’s followers, he said, deserve a celebration.

“After all these years, we owed it as much to them as to ourselves to celebrate this end, and give them something that produces a lot of pride in the brand that they love,” he said.

The Demon 170 is street legal, even though it comes with wide racing tires . To make it a daily driver, the company is offering a package of smaller, more street friendly wheels and tires.

At a devilish $96,666, the car comes standard with only a driver’s seat and a basic radio. But it has air conditioning. Front passenger and back seats are optional for $1 each. You can also get leather, a sunroof and a better sound system.

Stellantis will make only up to 3,300 of them, and Kuniskis isn’t sure if they’ll hit that number due to potential parts shortages and a limited production time.

If previous limited-edition models are any indication, the Demon 170 should become an instant classic collector’s car, Kuniskis said.

“If you look at some of the cars that we’ve had in our past, it’s pretty easy to tell which ones people want to collect,” he said. “A lot of times it’s the lower (sales) volume, extreme examples, whether its extreme looks or extreme performance. Well, this one happens to have both.”

© 2023 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed without permission.

Attackers have the ability not only to manipulate software, but also to tamper with the hardware. A team from Bochum is devising methods to detect such tampering.

Security gaps exist not only in software, but also directly in hardware. Attackers might deliberately have them built in in order to attack technical applications on a large scale. Researchers at Ruhr University Bochum, Germany, and the Max Planck Institute for Security and Privacy (MPI-SP) in Bochum are exploring methods of detecting such so-called hardware Trojans. They compared construction plans for chips with electron microscope images of real chips and had an algorithm search for differences. This is how they detected deviations in 37 out of 40 cases.

The team at the CASA Cluster of Excellence (short for Cyber Security in the Age of Large-Scale Adversaries), headed by Dr. Steffen Becker, and the MPI-SP team headed by Endres Puschner, will present their findings at the IEEE Symposium on Security and Privacy, which will take place in San Francisco from 22 to 25 May 2023. The research was conducted in collaboration with Thorben Moos from the Université catholique de Louvain (Belgium) and the Federal Criminal Police Office in Germany.

The researchers released all images of the chips, the design data as well as the analysis algorithms online for free so that other research groups can use the data to conduct further studies. A preprint of the paper is also published as part of the Proceedings of the IEEE Symposium on Security and Privacy.

Manufacturing plants as a gateway for hardware Trojans

These days, electronic chips are integrated into countless objects. They are more often than not designed by companies that don’t operate their own production facilities. The construction plans are therefore sent to highly specialized chip factories for production.

“It’s conceivable that tiny changes might be inserted into the designs in the factories shortly before production that could override the security of the chips,” explains Steffen Becker and gives an example for the possible consequences: “In extreme cases, such hardware Trojans could allow an attacker to paralyze parts of the telecommunications infrastructure at the push of a button.”

Identifying differences between chips and construction plans

Becker and Puschner’s team analyzed chips produced in the four modern technology sizes of 28, 40, 65 and 90 nanometers. For this purpose, they collaborated with Dr. Thorben Moos, who had designed several chips as part of his Ph.D. research at Ruhr University Bochum and had them manufactured. Thus, the researchers had both the design files and the manufactured chips at their disposal. They obviously couldn’t modify the chips after the fact and build in hardware Trojans. And so they employed a trick: rather than manipulating the chips, Thorben Moos changed his designs retroactively in order to create minimal deviations between the construction plans and the chips. Then, the Bochum researchers tested if they could detect these changes without knowing what exactly they had to look for and where.

In the first step, the team at Ruhr University Bochum and MPI-SP had to prepare the chips using complex chemical and mechanical methods in order to take several thousand images of the lowest chip layers with a scanning electron microscope. These layers contain several hundred thousand of the so-called standard cells that carry out logical operations.

“Comparing the chip images and the construction plans turned out to be quite a challenge, because we first had to precisely superimpose the data,” says Endres Puschner. In addition, every little impurity on the chip could block the view of certain sections of the image. “On the smallest chip, which is 28 nanometers in size, a single speck of dust or a hair can obscure a whole row of standard cells,” says the IT security expert.

Almost all manipulations detected

The researchers used image processing methods to carefully match standard cell for standard cell and looked for deviations between the construction plans and the microscopic images of the chips. “The results give cause for cautious optimism,” says Puschner.

For chip sizes of 90, 65 and 40 nanometers, the team successfully identified all modifications. The number of false-positive results totaled 500, i.e. standard cells were flagged as having been modified, although they were in fact untouched.

“With more than 1.5 million standard cells examined, this is a very good rate,” says Puschner. It was only with the smallest chip of 28 nanometers that the researchers failed to detect three subtle changes.

Higher detection rate through clean room and optimized algorithms

A better recording quality could remedy this problem in the future. “Scanning electron microscopes do exist that are specifically designed to take chip images,” points out Becker. Moreover, using them in a clean room where contamination can be prevented would increase the detection rate even further.

“We also hope that other groups will use our data for follow-up studies,” as Steffen Becker outlines potential future developments. “Machine learning could probably improve the detection algorithm to such an extent that it would also detect the changes on the smallest chips that we missed.”

In our daily lives, lithium-ion batteries have become indispensable. They function only because of a passivation layer that forms during their initial cycle. As researchers at Karlsruhe Institute of Technology (KIT) found out via simulations, this solid electrolyte interphase develops not directly at the electrode but aggregates in the solution. Their study has been published in the journal Advanced Energy Materials. Their findings allow the optimization of the performance and lifetime of future batteries.

From smartphones to electric cars—wherever a mobile energy source is required—it is almost always a lithium-ion battery that does the job. An essential part of the reliable function of this and other liquid electrolyte batteries is the solid electrolyte interphase (SEI). This passivation layer forms when voltage is applied for the first time. The electrolyte is being decomposed in the immediate vicinity of the surface. Until now, it remained unclear how the particles in the electrolytes form a layer that is up to 100 nanometers thick on the surface of the electrode since the decomposition reaction is only possible within a few nanometers distance from the surface.

The passivation layer on the anode surface is crucial to the electrochemical capacity and lifetime of a lithium-ion battery because it is highly stressed with every charging cycle. When the SEI is broken up during this process, the electrolyte is further decomposed and the battery’s capacity is reduced—a process that determines the lifetime of a battery. With the right knowledge of the SEI’s growth and composition, the properties of a battery can be controlled. But so far, no experimental or computer-aided approach was sufficient to decipher the SEI’s complex growth processes that take place on a very wide scale and in different dimensions.

Researchers at the KIT Institute of Nanotechnology (INT) have now managed to characterize the formation of the SEI with a multi-scale approach. “This solves one of the great mysteries regarding an essential part of all liquid electrolyte batteries—especially the lithium-ion batteries we all use every day,” says Professor Wolfgang Wenzel, director of the research group “Multiscale Materials Modelling and Virtual Design” at INT, which is involved in the large-scale European research initiative BATTERY 2030+ that aims to develop safe, affordable, long-lasting, sustainable high-performance batteries for the future.

More than 50,000 simulations for different reaction conditions

To examine the growth and composition of the passivation layer at the anode of liquid electrolyte batteries, the researchers at INT generated an ensemble of more than 50,000 simulations representing different reaction conditions. They found that the growth of the organic SEI follows a solution-mediated pathway. First, SEI precursors that are formed directly at the surface join far away from the electrode surface via a nucleation process. The subsequent rapid growth of the nuclei leads to the formation of a porous layer that eventually covers the electrode surface.

These findings offer a solution to the paradoxical situation that SEI constituents can form only near the surface, where electrons are available, but their growth would stop once this narrow region is covered. “We were able to identify the key reaction parameters that determine SEI thickness,” explains Dr. Saibal Jana, postdoc at INT and one of the authors of the study.

“This will enable the future development of electrolytes and suitable additives that control the properties of the SEI and optimize the battery’s performance and lifetime.”

Countless historical photographs are stored in black and white in the world’s archives. Today, these cultural assets and contemporary documents are conserved by means of digitization and (partially) improved through digital restoration.

Efforts have been made since the 1970s to colorize this film footage. However, there was no real progress—the effort and the associated costs for manual or semi-automatic coloring techniques were too high. It is possible in principle to color films fully automatically. However, this has the disadvantage that the colors may be nice to look at, but they are not true to reality.

This is exactly where the project “RE:Color: Efficient coloring of films in cinema quality based on novel machine learning methods” at Graz University of Technology (TU Graz) came in. Computer scientists led by Thomas Pock from the Institute of Computer Graphics and Vision together with the Graz-based company specialized in the restoration of historical films HS-Art have developed an integrated software application that combines interactive and automated coloring techniques with deep learning technologies.

The result is an algorithm for a predominantly automatic, yet fully user-controlled coloring process.

Realistic coloring

According to Pock, it is essential that humans can influence the coloring process: “You always need someone who is familiar with the historical traditions who can say what the clothes, the facades, etc. looked like back then. Was the soldier’s uniform green or blue? No algorithm can decide that, but it can learn from it.”

The algorithm must therefore be fed with a sufficiently large collection of training samples in order to then automatically take over the coloring of historical films. “It’s about coloring the films as efficiently as possible with as little user input as possible. This can mean, for instance, that a person specifies the coloring for a film frame and the software then takes over the coloring of further frames,” explains Pock.

This central requirement of user-guided control is only fulfilled thanks to pre-trained neural networks that can be dynamically influenced by user interaction.

For this purpose, the researchers have developed different novel approaches in the field of automated coloring based on artificial intelligence (AI). Together with the developers of HS-Art, they then implemented the most efficient approach in a prototype application and generated a sufficiently powerful collection of training samples. Then the implementation of human-guided control took place to obtain authentic and appropriate color schemes.

Authentic image noise

With the algorithms developed, the films can be restored extremely cleanly and also colored—but this is not always necessarily desirable. Pock says, “With historical footage and cinema films in general, you need a certain amount of noise, so-called ‘film grain’, otherwise it doesn’t look authentic to the audience. For this reason, the software can also artificially generate and add this noise again after restoration and coloring.”

The core algorithm was published at a major international symposium, and the source code is freely available. For efficient use, however, software based on this, which was developed by the project partner HS-Art and is in their product portfolio, is necessary. The “Diamant film colorizer” was used, for example, in the ZDFzeit documentary series “Hitler’s Power” to color historical footage true to the original.

The BBC said Monday that it had told staff to delete Chinese-owned video app TikTok unless it was needed for business reasons, with Western institutions increasingly taking a harder stance over data collection fears.

The British broadcasting giant reported that it sent staff a message on Sunday saying: “We don’t recommend installing TikTok on a BBC corporate device unless there is a justified business reason.

“If you do not need TikTok for business reasons, TikTok should be deleted,” it added.

Western authorities have been taking an increasingly firm approach to the app, owned by the firm ByteDance, citing fears that user data could be used or abused by Chinese officials.

The UK on Thursday announced a security ban on TikTok on government devices, in line with action by the European Union and the United States.

The BBC told AFP on Monday that it “takes the safety and security of our systems, data and people incredibly seriously”.

It added that while usage of TikTok on its corporate devices is still permitted for editorial and marketing purposes, “we will continue to monitor and assess the situation”.

The broadcaster has launched multiple pages on the app as it attempts to reach new audiences, and its official account has 4.4 million followers.

ByteDance has long insisted that it does not keep data in China or share it with Beijing.

© 2023 AFP

Researchers have not yet gotten the additive manufacturing (or 3D printing) of metals down to a science completely. Gaps in our understanding of what happens within metal during the process have made results inconsistent. But a new breakthrough could grant an unprecedented level of mastery over metal 3D printing.

Using two different particle accelerator facilities, researchers at the National Institute of Standards and Technology (NIST), KTH Royal Institute of Technology in Sweden and other institutions have peered into the internal structure of steel as it was melted and then solidified during 3D printing. The findings, published in Acta Materialia, unlock a computational tool for 3D-printing professionals, offering them a greater ability to predict and control the characteristics of printed parts, potentially improving the technology’s consistency and feasibility for large-scale manufacturing.

A common approach for printing metal pieces involves essentially welding pools of powdered metal with lasers, layer by layer, into a desired shape. During the first steps of printing with a metal alloy, wherein the material rapidly heats up and cools off, its atoms—which can be a smattering of different elements—pack into ordered, crystalline formations. The crystals determine the properties, such as toughness and corrosion resistance, of the printed part. Different crystal structures can emerge, each with their own pros and cons.

“Basically, if we can control the microstructure during the initial steps of the printing process, then we can obtain the desired crystals and, ultimately, determine the performance of additively manufactured parts,” said NIST physicist Fan Zhang, a study co-author.

While the printing process wastes less material and can be used to produce more complicated shapes than traditional manufacturing methods, researchers have struggled to grasp how to steer metal toward particular kinds of crystals over others.

This lack of knowledge has led to less than desirable results, such as parts with complex shapes cracking prematurely thanks to their crystal structure.

“Among the thousands of alloys that are commonly manufactured, only a handful can be made using additive manufacturing,” Zhang said.

Part of the challenge for scientists has been that solidification during metal 3D printing occurs in the blink of an eye.

To capture the high-speed phenomenon, the authors of the new study employed powerful X-rays generated by cyclic particle accelerators, called synchrotrons, at Argonne National Laboratory’s Advanced Photon Source and the Paul Scherrer Institute’s Swiss Light Source.

The team sought to learn how the cooling rates of metal, which can be controlled by laser power and movement settings, influence crystal structure. Then the researchers would compare the data to the predictions of a widely used computational model developed in the 1980s that describes the solidification of alloys.

While the model is trusted for traditional manufacturing processes, the jury has been out on its applicability in the unique context of 3D printing’s rapid temperature shifts.

“Synchrotron experiments are time consuming and expensive, so you cannot run them for every condition that you’re interested in. But they are very useful for validating models that you then can use to simulate the interesting conditions,” said study co-author Greta Lindwall, an associate professor of materials science and engineering at KTH Royal Institute of Technology.

Within the synchrotrons, the authors set up additive manufacturing conditions for hot-work tool steel—a kind of metal used to make, as the name suggests, tools that can withstand high temperatures.

As lasers liquified the metal and different crystals emerged, X-ray beams probed the samples with enough energy and speed to produce images of the fleeting process. The team members required two separate facilities to support the cooling rates they wanted to test, which ranged from temperatures of tens of thousands to more than a million kelvins per second.

The data the researchers collected depicted the push and pull between two kinds of crystal structures, austenite and delta ferrite, the latter being associated with cracking in printed parts. As cooling rates surpassed 1.5 million kelvins (2.7 million degrees Fahrenheit) per second, austenite began to dominate its rival. This critical threshold lined up with what the model foretold.

“The model and the experimental data are nicely in agreement. When we saw the results, we were really excited,” Zhang said.

The model has long been a reliable tool for materials design in traditional manufacturing, and now the 3D-printing space may be afforded the same support.

The results indicate that the model can inform scientists and engineers on what cooling rates to select for the early solidification steps of the printing process. That way the optimal crystal structure would appear within their desired material, making metal 3D printing less of a roll of the dice.

“If we have data, we can use it to validate the models. That’s how you accelerate the widespread adoption of additive manufacturing for industrial use,” Zhang said.

This story is republished courtesy of NIST. Read the original story here.

You have just returned home after a long day at work and are about to sit down for dinner when suddenly your phone starts buzzing. On the other end is a loved one, perhaps a parent, a child or a childhood friend, begging you to send them money immediately.

You ask them questions, attempting to understand. There is something off about their answers, which are either vague or out of character, and sometimes there is a peculiar delay, almost as though they were thinking a little too slowly. Yet, you are certain that it is definitely your loved one speaking: That is their voice you hear, and the caller ID is showing their number. Chalking up the strangeness to their panic, you dutifully send the money to the bank account they provide you.

The next day, you call them back to make sure everything is all right. Your loved one has no idea what you are talking about. That is because they never called you—you have been tricked by technology: a voice deepfake. Thousands of people were scammed this way in 2022.

As computer security researchers, we see that ongoing advancements in deep-learning algorithms, audio editing and engineering, and synthetic voice generation have meant that it is increasingly possible to convincingly simulate a person’s voice.

Even worse, chatbots like ChatGPT are starting to generate realistic scripts with adaptive real-time responses. By combining these technologies with voice generation, a deepfake goes from being a static recording to a live, lifelike avatar that can convincingly have a phone conversation.

Cloning a voice

Crafting a compelling high-quality deepfake, whether video or audio, is not the easiest thing to do. It requires a wealth of artistic and technical skills, powerful hardware and a fairly hefty sample of the target voice.

There are a growing number of services offering to produce moderate- to high-quality voice clones for a fee, and some voice deepfake tools need a sample of only a minute long, or even just a few seconds, to produce a voice clone that could be convincing enough to fool someone. However, to convince a loved one—for example, to use in an impersonation scam—it would likely take a significantly larger sample.

Protecting against scams and disinformation

With all that said, we at the DeFake Project of the Rochester Institute of Technology, the University of Mississippi and Michigan State University, and other researchers are working hard to be able to detect video and audio deepfakes and limit the harm they cause. There are also straightforward and everyday actions that you can take to protect yourself.

For starters, voice phishing, or “vishing,” scams like the one described above are the most likely voice deepfakes you might encounter in everyday life, both at work and at home. In 2019, an energy firm was scammed out of US$243,000 when criminals simulated the voice of its parent company’s boss to order an employee to transfer funds to a supplier. In 2022, people were swindled out of an estimated $11 million by simulated voices, including of close, personal connections.

What can you do?

Be mindful of unexpected calls, even from people you know well. This is not to say you need to schedule every call, but it helps to at least email or text message ahead. Also, do not rely on caller ID, since that can be faked, too. For example, if you receive a call from someone claiming to represent your bank, hang up and call the bank directly to confirm the call’s legitimacy. Be sure to use the number you have written down, saved in your contacts list or that you can find on Google.

Additionally, be careful with your personal identifying information, like your Social Security number, home address, birth date, phone number, middle name and even the names of your children and pets. Scammers can use this information to impersonate you to banks, realtors and others, enriching themselves while bankrupting you or destroying your credit.

Here is another piece of advice: know yourself. Specifically, know your intellectual and emotional biases and vulnerabilities. This is good life advice in general, but it is key to protect yourself from being manipulated. Scammers typically seek to suss out and then prey on your financial anxieties, your political attachments or other inclinations, whatever those may be.

This alertness is also a decent defense against disinformation using voice deepfakes. Deepfakes can be used to take advantage of your confirmation bias, or what you are inclined to believe about someone.

If you hear an important person, whether from your community or the government, saying something that either seems very uncharacteristic for them or confirms your worst suspicions of them, you would be wise to be wary.

This article is republished from The Conversation under a Creative Commons license. Read the original article.