Using the Atacama Large Millimeter/submillimeter Array (ALMA), a team of astronomers led by Abubakar Fadul from the Max Planck Institute for Astronomy (MPIA) has discovered complex organic molecules—including the first tentative detection of ethylene glycol and glycolonitrile—in the protoplanetary disk of the outbursting protostar V883 Orionis.
We know magnetism as a fundamental force of nature that plays a crucial role in both the natural world and modern technology. It governs the behavior of materials at the atomic level and is essential for the functioning of countless devices in our everyday life, including data storage, sensing, wireless charging, sound recording and playing systems, and more.
Hi-tech surveillance technologies are a double-edged sword. On the one hand, you want sophisticated devices to detect suspicious behavior and alert authorities. But on the other, there is the need to protect individual privacy. Balancing public safety and personal freedoms is an ongoing challenge for innovators and policymakers.
This debate is set to reignite with news that researchers at La Sapienza University in Rome have developed a system that can identify individuals just by the way they disrupt Wi-Fi signals.
The scientists have dubbed this new technology “WhoFi.” Unlike traditional biometric systems such as fingerprint scanners and facial recognition, it doesn’t require direct physical contact or visual feeds. WhoFi can also track individuals in a larger area than a fixed-position camera, provided there is a Wi-Fi network.
The development of more advanced technologies to process radiofrequency signals could further advance wireless communication, allowing devices connected to the internet to share information with each other faster and while consuming less energy. Currently, radio frequency signals are processed using software-defined radios (SDRs), systems that can modulate, filter and analyze signals using software rather than hardware components.
Despite their widespread use, these systems rely on purely digital hardware in which computing and memory modules are physically separated, leading to constant data shuttling between the two and hence extra energy consumption. Furthermore, the extensive use of circuit components known as analog-to-digital converters (ADCs), which convert incoming radiofrequency signals into digital values that can then be processed by digital computers, often results in processing delays (i.e., latency) and substantial energy consumption. Electronics engineers have thus been trying to develop alternative systems that can directly manipulate signals in their original (i.e., analog) form, which would reduce the movement of data and lower energy consumption.
Researchers at the University of Massachusetts Amherst, Texas A&M University and TetraMem Inc. recently introduced a promising new system for processing analog radiofrequency systems, which is based on non-volatile memory devices known as memristors integrated on a chip. Their proposed system, presented in a paper in Nature Electronics, was found to process radiofrequency signals significantly faster and more energy-efficiently than existing SDRs.
Researchers investigating atomic-scale phenomena impacting next-generation electronic and quantum devices have captured the first microscopy images of atomic thermal vibrations, revealing a new type of motion that could reshape the design of quantum technologies and ultrathin electronics.
Yichao Zhang, an assistant professor in the University of Maryland Department of Materials Science and Engineering, has developed an electron microscopy technique to directly image “moiré phasons”—a physical phenomenon that impacts superconductivity and heat conduction in two-dimensional materials for next-generation electronic and quantum devices.
A paper about the research, which documents images of the thermal vibration of individual atoms for the first time, has been published in the journal Science.
Bipolar disorder is a mental health condition characterized by extreme mood swings, with alternating periods of depression and manic episodes. Past research suggests that bipolar disorder has a strong genetic component and is among the most heritable psychiatric disorders.
To better understand the genetic factors that increase the risk of developing this mental health disorder, neuroscientists and geneticists have carried out various genome-wide association studies (GWAS). These are essentially studies aimed at identifying specific regions of the human genome that are linked with an increased risk of having bipolar disorder, also referred to as bipolar risk loci.
While earlier works have identified many of these regions, causal single nucleotide polymorphisms (SNPs) for the disorder are largely unknown. These are essentially genetic variants that primarily contribute to bipolar disorder risk, as opposed to just being mere markers of it.
For the first time, researchers at the Netherlands Institute for Neuroscience and Amsterdam UMC have identified what happens in neural networks deep within the brain during obsessive thoughts and compulsive behaviors. Using electrodes implanted in the brain, they observed how specific brain waves became active. These brain waves serve as a biomarker for obsessive-compulsive disorder (OCD) and are an important step towards more targeted treatments.
OCD is a psychiatric disorder in which people suffer from obsessive thoughts (obsessions) and compulsive behaviors (compulsions). A well-known example is fear of contamination: someone is constantly afraid of becoming infected (the obsession) and feels compelled to wash their hands over and over again (the compulsion).
In OCD, communication appears to be disrupted between the cerebral cortex, the striatum, and the thalamus, areas of the brain that together form the CSTC circuit. Normally, this circuit mainly coordinates movement and motivation.
For the first time, spin waves, also known as magnons, have been directly observed at the nanoscale. This breakthrough was made possible by combining a high–energy-resolution electron microscope with a theoretical method developed at Uppsala University. The results open exciting new opportunities for studying and controlling magnetism at the nanoscale.
Researchers from Trinity College Dublin’s School of Engineering have built a powerful new machine that lets us watch precisely what happens when tiny particles—far smaller than a grain of sand—hit a surface at extremely high speeds. It’s the only machine like it in Europe, and it took over two years to design and build.
Scientists at the University of Stuttgart’s Institute of Aerodynamics and Gas Dynamics (IAG) have produced a novel dataset that will improve the development of turbulence models. With the help of the Hawk supercomputer at the High-Performance Computing Center Stuttgart (HLRS), investigators in the laboratory of Dr. Christoph Wenzel conducted a large-scale direct numerical simulation of a spatially evolving turbulent boundary layer.
Using more than 100 million CPU hours on Hawk, the simulation is unique in that it captures the onset of a canonical, fully-developed turbulent state in a single computational domain. The study also identified with unprecedented clarity an inflection point at which the outer region of the turbulent boundary layer begins to maintain a self-similar structure as it moves toward high Reynolds numbers. The results appear in a new paper published in the Journal of Fluid Mechanics.
“Our team’s goal is to understand unexplored parameter regimes in turbulent boundary layers,” said Jason Appelbaum, a Ph.D. candidate in the Wenzel Lab and leader of this research. “By running a large-scale simulation that fully resolves the entire development of turbulence from an early to an evolved state, we have generated the first reliable, full-resolution dataset for investigating how high-Reynolds-number effects emerge.”