Aurelia Butler

For “Star Wars” fans, the streaking stars seen from the cockpit of the Millennium Falcon as it jumps to hyperspace is a canonical image. But what would a pilot actually see if she could accelerate in an instant through the vacuum of space? According to a prediction known as the Unruh effect, she would more likely see a warm glow.
Since the 1970s when it was first proposed, the Unruh effect has eluded detection, mainly because the probability of seeing the effect is infinitesimally small, requiring either enormous accelerations or vast amounts of observation time. But researchers at MIT and the University of Waterloo believe they have found a way to significantly increase the probability of observing the Unruh effect, which they detail in a study appearing in Physical Review Letters.
Rather than observe the effect spontaneously as others have attempted in the past, the team proposes stimulating the phenomenon, in a very particular way that enhances the Unruh effect while suppressing other competing effects. The researchers liken their idea to throwing an invisibility cloak over other conventional phenomena, which should then reveal the much less obvious Unruh effect.
If it can be realized in a practical experiment, this new stimulated approach, with an added layer of invisibility (or “acceleration-induced transparency,” as described in the paper) could vastly increase the probability of observing the Unruh effect. Instead of waiting longer than the age of the universe for an accelerating particle to produce a warm glow as the Unruh effect predicts, the team’s approach would shave that wait time down to a few hours.
“Now at least we know there is a chance in our lifetimes where we might actually see this effect,” says study co-author Vivishek Sudhir, assistant professor of mechanical engineering at MIT, who is designing an experiment to catch the effect based on the group’s theory. “It’s a hard experiment, and there’s no guarantee that we’d be able to do it, but this idea is our nearest hope.”
The study’s co-authors also include Barbara Šoda and Achim Kempf of the University of Waterloo. More

An artificial intelligence (AI) tool developed by Cedars-Sinai investigators accurately predicted who would develop pancreatic cancer based on what their CT scan images looked like years prior to being diagnosed with the disease. The findings, which may help prevent death through early detection of one of the most challenging cancers to treat, are published in the journal Cancer Biomarkers.
“This AI tool was able to capture and quantify very subtle, early signs of pancreatic ductal adenocarcinoma in CT scans years before occurrence of the disease. These are signs that the human eye would never be able to discern,” said Debiao Li, PhD, director of the Biomedical Imaging Research Institute, professor of Biomedical Sciences and Imaging at Cedars-Sinai, and senior and corresponding author of the study. Li is also the Karl Storz Chair in Minimally Invasive Surgery in Honor of George Berci, MD.
Pancreatic ductal adenocarcinoma is not only the most common type of pancreatic cancer, but it’s also the most deadly. Less than 10% of people diagnosed with the disease live more than five years after being diagnosed or starting treatment. But recent studies have reported that finding the cancer early can increase survival rates by as much as 50%. There currently is no easy way to find pancreatic cancer early, however.
People with this type of cancer may experience symptoms such as general abdominal pain or unexplained weight loss, but these symptoms are often ignored or overlooked as signs of the cancer since they are common in many health conditions.
“There are no unique symptoms that can provide an early diagnosis forpancreatic ductal adenocarcinoma,” said Stephen J. Pandol, MD, director of Basic and Translational Pancreas Research and program director of the Gastroenterology Fellowship Program at Cedars-Sinai, and another author of the study. “This AI tool may eventually be used to detect early disease in people undergoing CT scans for abdominal pain or other issues.”
The investigators reviewed electronic medical records to identify people who were diagnosed with the cancer within the last 15 years and who underwent CT scans six months to three years prior to their diagnosis. These CT images were considered normal at the time they were taken. The team identified 36 patients who met these criteria, the majority of whom had CT scans done in the ER because of abdominal pain.
The AI tool was trained to analyze these pre-diagnostic CT images from people with pancreatic cancer and compare them with CT images from 36 people who didn’t develop the cancer. The investigators reported that the model was 86% accurate in identifying people who would eventually be found to have pancreatic cancer and those who would not develop the cancer.
The AI model picked up on variations on the surface of the pancreas between people with cancer and healthy controls. These textural differences could be the result of molecular changes that occur during the development of pancreatic cancer.
“Our hope is this tool could catch the cancer early enough to make it possible for more people to have their tumor completely removed through surgery,” said Touseef Ahmad Qureshi, PhD, a scientist at Cedars-Sinai and first author of the study.
The investigators are currently collecting data from thousands of patients at healthcare sites throughout the U.S. to continue to study the AI tool’s prediction capability.
Funding: The study was funded by the Board of Counselors of Cedars-Sinai Medical Center, the Cedars-Sinai Samuel Oschin Comprehensive Cancer Institute and the National Institutes of Health under award number R01 CA260955.
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The COVID-19 pandemic and its public response created large shifts in how people travel. In some areas, these restrictions on travel appear to have had little effect on air pollution, and some cities have worse air quality than ever.
In Chaos, by AIP Publishing, researchers in China created a network model drawn from the traffic index and air quality index of 21 cities across six regions in their country to quantify how traffic emissions from one city affect another. They wanted to leverage data from COVID-19 lockdown procedures to better explain the relationship between traffic and air pollution and saw the COVID-19 lockdowns as a rare opportunity for research.
“Air pollution is a typical ‘commons governance’ issue,” said author Jingfang Fan. “The impact of the pandemic has led cities to implement different traffic restriction policies, one after another, which naturally forms a controlled experiment to reveal their relationship.”
To address these questions, they turned to a weighted climate network framework to model each city as a node using pre-pandemic data from 2019 and data from 2020. They added a two-layer network that incorporated different regions, lockdown stages, and outbreak levels.
Surrounding traffic conditions influenced air quality in Beijing-Tianjin-Hebei, the Chengdu-Chongqing Economic Circle, and central China after the outbreak. Pollution tended to peak in cities as they made initial progress for containing the virus.
During this time, pollution in Beijing-Tianjin-Hebei and central China lessened over time. Beijing-Tianjin-Hebei, however, saw another spike as control measures for outbound traffic from Wuhan and Hubei were lifted.
“Air pollution in big cities, such as Beijing and Shanghai, is more affected by other cities,” said author Saini Yang. “This is contrary to what we generally think, that air pollution in big cities is mainly caused by its own conditions, including the traffic congestion.”
Author Weiping Wang hopes the team’s work inspires other interdisciplinary teams to explore unique ways to explore problems in environmental science. They will look to improve their model with a higher degree of detail for traffic emissions.
“Our discovery is that in order to improve air pollution, it is not only necessary to improve and reduce our own urban traffic and increase green travel, but also need the joint efforts of surrounding cities,” said author Na Ying. “Everyone is important in the governance of commons.”
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Researchers have shown that the UK’s existing copper network cables can support faster internet speeds, but only to a limit. They say additional investment is urgently needed if the government is serious about its commitment to making high-speed internet available to all.
The researchers, from the University of Cambridge and BT, have established the maximum speed at which data can be transmitted through existing copper cables. This limit would allow for faster internet compared to the speeds currently achievable using standard infrastructure, however it will not be able to support high-speed internet in the longer term.
The team found that the ‘twisted pair’ copper cables that reach every house and business in the UK are physically limited in their ability to support higher frequencies, which in turn support higher data rates.
While full-fibre internet is currently available to around one in four households, it is expected to take at least two decades before it reaches every home in the UK. In the meantime, however, existing infrastructure can be improved to temporarily support high-speed internet.
The results, reported in the journal Nature Communications, both establish a physical limit on the UK’s ubiquitous copper cables, and emphasise the importance of immediate investment in future technologies.
The Cambridge-led team used a combination of computer modelling and experiments to determine whether it was possible to get higher speeds out of existing copper infrastructure and found that it can carry a maximum frequency of about 5 GHz, above the currently used spectrum, which is lower than 1 GHz. Above 5 GHz however, the copper cables start to behave like antennas. More

Semiconducting nanomaterials with 3D network structures have high surface areas and lots of pores that make them excellent for applications involving adsorbing, separating, and sensing. However, simultaneously controlling the electrical properties and creating useful micro- and macro-scale structures, while achieving excellent functionality and end-use versatility, remains challenging. Now, Osaka University researchers, in collaboration with The University of Tokyo, Kyushu University, and Okayama University, have developed a nanocellulose paper semiconductor that provides both nano-micro-macro trans-scale designability of the 3D structures and wide tunability of the electrical properties. Their findings are published in ACS Nano.
Cellulose is a natural and easy to source material derived from wood. Cellulose nanofibers (nanocellulose) can be made into sheets of flexible nanocellulose paper (nanopaper) with dimensions like those of standard A4. Nanopaper does not conduct an electric current; however, heating can introduce conducting properties. Unfortunately, this exposure to heat can also disrupt the nanostructure.
The researchers have therefore devised a treatment process that allows them to heat the nanopaper without damaging the structures of the paper from the nanoscale up to the macroscale.
“An important property for the nanopaper semiconductor is tunability because this allows devices to be designed for specific applications,” explains study author Hirotaka Koga. “We applied an iodine treatment that was very effective for protecting the nanostructure of the nanopaper. Combining this with spatially controlled drying meant that the pyrolysis treatment did not substantially alter the designed structures and the selected temperature could be used to control the electrical properties.”
The researchers used origami (paper folding) and kirigami (paper cutting) techniques to provide playful examples of the flexibility of the nanopaper at the macrolevel. A bird and box were folded, shapes including an apple and snowflake were punched out, and more intricate structures were produced by laser cutting. This demonstrated the level of detail possible, as well as the lack of damage caused by the heat treatment.
Examples of successful applications showed nanopaper semiconductor sensors incorporated into wearable devices to detect exhaled moisture breaking through facemasks and moisture on the skin. The nanopaper semiconductor was also used as an electrode in a glucose biofuel cell and the energy generated lit a small bulb.
“The structure maintenance and tunability that we have been able to show is very encouraging for the translation of nanomaterials into practical devices,” says Associate Professor Koga. “We believe that our approach will underpin the next steps in sustainable electronics made entirely from plant materials.”
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An international research team has achieved an important milestone in the quest for high density, low-power consuming nonvolatile magnetic memory.
“We established a new method to enable magnetization reversal on perpendicularly magnetized ferromagnets — without the need for an external magnetic field,” said Makoto Kohda, co-author of the study and professor at Tohoku University’s Graduate School of Engineering.
Spintronic devices optimize the intrinsic spin of electrons and their associated magnetic movement. With society needing better performing electronics with less power consumption, spintronics will play a large part in next-generation nanoelectronic devices.
A spin current converted from a charge current creates a spin-orbit torque (SOT) on ferromagnets, enabling electrical control of the magnetization. Currently, this is done unidirectionaly and external magnetic fields must be used to switch perpendicular magnetized ferromagnets. So-called field free switching, along with diminished current density for reduced energy consumption, is essential for commercial viability.
Kohda and his team comprised Professor Emeritus Junsaku Nitta from Tohoku University’s Graduate School of Engineering and colleagues from the Korea Advanced Institute of Science and Technology (KAIST), such as researcher Jeonchun Ryu, professor Byong-Guk Park and professor Kyung-Jin Lee.
They harnessed spin generated in all directions to create field free switching using polycrystalline CoFeB/Ti/CoFeB — crucial because this material is already employed in the mass production of spintronic devices. Furthermore, the new method brought about a 30% lower current density than existing spin current based magnetization reversal.
“International collaboration is the key for demonstrating next-generation technology in nonvolatile memory. The next step for us will be to apply this principle to spintronic devices’ mass production to help usher in the power-saving technology required for IoT and AI,” added Kohda.
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When you live in the driest State in the driest country in the world, bushfires are an unfortunate, and all-too-regular part of life. Learning how to survive such emergencies is important for all people, but especially for our youngest citizens
Now, a new virtual reality (VR) experience developed by the University of South Australia is educating children about bushfires and helping them learn how to be safer in a bushfire incident.
Focusing on children aged 10-12 years, the new VR experience presents a scenario where children are tasked to look after a friend’s dog just before a fire event begins to unfold. They participate in a series of problem-solving activities to help save and protect themselves and the dog.
Published in the Journal of Educational Computing, the research demonstrates how immersive VR experiences can deliver significant positive learning outcomes for primary children, independent of their gender, background knowledge or perceived ability to respond to bushfire hazards.
The findings showed that more than 80 per cent of children agreed or strongly agreed that they felt more confident to calmly evaluate the options and make wise decisions to protect themselves from a bushfire. This is especially significant considering that 91 per cent of participants originally lacked any knowledge of fires, and that 67 per cent had said that they were too young to make safety decisions in a fire.
The project was part of Safa Molan’s PhD project. Her supervisor and fellow researcher, UniSA’s Professor Delene Weber says immersive VR experiences have enormous potential to engage, educate and empower younger generations. More

Researchers at the Max Planck Institute for Intelligent Systems (MPI-IS), Cornell University and Shanghai Jiao Tong University have developed collectives of microrobots which can move in any desired formation. The miniature particles are capable of reconfiguring their swarm behavior quickly and robustly. Floating on the surface of water, the versatile microrobotic discs can go round in circles, dance the boogie, bunch up into a clump, spread out like gas or form a straight line like beads on a string.
Each robot is slightly bigger than a hair’s width. They are 3D printed using a polymer and then coated with a thin top layer of cobalt. Thanks to the metal the microrobots become miniature magnets. Meanwhile, wire coils which create a magnetic field when electricity flows through them surround the setup. The magnetic field allows the particles to be precisely steered around a one-centimeter-wide pool of water. When they form a line, for instance, the researchers can move the robots in such a way that they “write” letters in the water. The research project of Gaurav Gardi and Prof. Metin Sitti from MPI-IS, Steven Ceron and Prof. Kirstin Petersen from Cornell University and Prof. Wendong Wang from Shanghai Jiao Tong University titled “Microrobot Collectives with Reconfigurable Morphologies, Behaviors, and Functions” was published in Nature Communications on April 26, 2022.
Collective behavior emerges from the interactions between the robots
Collective behavior and swarm patterns are found everywhere in nature. A flock of birds exhibits swarm behavior, as does a school of fish. Robots can also be programmed to act in swarms — and have been seen doing so quite prominently. A technology company recently presented a drone lightshow that won the company a Guinness World Record by programming several hundred drones and flying them side-by-side, creating amazing patterns in the night sky. Each drone in this swarm was equipped with computational power steering it in every possible direction. But what if the single particle is so tiny that computation isn’t an option? When a robot is just 300 micrometers wide, one cannot program it with an algorithm.
Three different forces are at play to compensate for the lack of computation. One is the magnetic force. Two magnets with opposite poles attract. Two identical poles repel each other. The second force is the fluid environment; the water around the discs. When particles swim in a swirl of water, they displace the water and affect the other surrounding particles in the system. The speed of the swirl and its magnitude determine how the particles interact. Thirdly, if two particles float next to each other, they tend to drift towards each other: they bend the water surface in such a way that they slowly come together. Scientists and cereal lovers call this the cheerio effect: if you let two cheerios float on milk, they will soon bump into each other. On the flip side, this effect can also cause two things to repel each other (try a hairpin and a cheerio).
Three forces allow for reconfigurability
The scientists use all three forces to create a coordinated, collective pattern of motion for several dozen microrobots as one system. A video (https://youtu.be/q91AWmTBzG8) shows how the scientists steer the robots through a parcour, displaying the formation that best suits the obstacle course, e.g. when they enter a narrow passage, the microrobots line up in single file and disperse again when they come out. The scientists can also make the robots dance, alone or as pairs. Additionally, they show how they put a tiny plastic ball into the water container and then aggregate the robots into a clump to push the floating ball along. They can place the tiny particles inside two gears and move the particles in a way that causes both gears to rotate. A more ordered pattern is also possible with each particle keeping an identical distance to its neighbor. All these different locomotion modes and formations are achieved through external computation: an algorithm is programmed to create a rotating or oscillating magnetic field which triggers the desired movement and reconfigurability.
“Depending on how we change the magnetic fields, the discs behave in a different way. We are tuning one force and then another until we get the movement we want. If we rotate the magnetic field within the coils too vigorously, the force which is causing the water to move around is too strong and the discs move away from each other. If we rotate too slow, then the cheerio effect which attracts the particles is too strong. We need to find the balance between the three,” Gaurav Gardi explains. He is a Ph.D. student in the Physical Intelligence department at MPI-IS and one of the two lead authors of the publication together with Steven Ceron from Cornell University.
A model for future biomedical and environmental applications
The future scenario for such microrobotic collectives is to go even smaller. “Our vision is to develop a system that is even tinier, made of particles only one micrometer small. These collectives could potentially go inside the human body and navigate through complex environments to deliver drugs, for instance, to block or unblock passages, or to stimulate a hard-to-reach area,” Gardi says.
“Robot collectives with robust transitions between locomotion behaviors are very rare. However, such versatile systems are advantageous to operate in complex environments. We are very happy we succeeded in developing such a robust and on-demand reconfigurable collective. We see our research as a blueprint for future biomedical applications, minimally invasive treatments, or environmental remediation,” adds Metin Sitti, who leads the Physical Intelligence Department and is a pioneer in the field of small-scale robotics and physical intelligence. More