(It is 18F outside.) Luckily, the air conditioner in my home office can handle as low as 0F outside.)
Researchers announced that they have achieved the world’s first elucidation of how hydrogen produces free electrons through the interaction with certain defects in silicon. The achievement has the potential to improve how insulated gate bipolar transistors (IGBTs) are designed and manufactured, making them more efficient and reducing their power loss. It is also expected to open up possibilities for future devices using ultra-wide bandgap (UWBG) materials.
In the global drive toward carbon neutrality, efforts to make power electronics more efficient and energy-saving are accelerating worldwide. IGBTs are key components responsible for power conversion, so improving their efficiency is a major priority. While hydrogen ion implantation has been used for about half a century to control electron concentration in silicon, the underlying mechanism has remained unclear until now.
In 2023, Mitsubishi Electric and University of Tsukuba jointly discovered a defect complex in silicon that contributes to increasing electron concentration. They confirmed that this complex is formed when an interstitial silicon pair and hydrogen bind, but the reason why free electrons are newly generated in this process was still unclear.
Wildfires are devastating events that destroy forests, burn homes and force people to leave their communities. They also have a profound impact on local ecosystems. But there is another problem that has been largely overlooked until now. When rain falls on the charred landscapes, it increases surface runoff and soil erosion that can last for decades, according to a new study published in Nature Geoscience.
On average, wildfires burn approximately 4 million square kilometers of land per year, an area equivalent to the size of the European Union. Despite this, there hasn’t been a global long-term assessment of how these fires affect soil erosion over time. So researchers from the European Commission’s Joint Research Center and the University of Basel, Switzerland, studied two decades’ worth of data to compile the world’s first global map of post-fire soil erosion.
The team used a sophisticated computer model called RUSLE (Revised Universal Soil Loss Equation), which they adapted for post-fire conditions to calculate how much soil moves based on factors such as vegetation cover and rainfall intensity. They combined this with satellite data of global wildfires from 2001 to 2019 and compared these areas with how the land looked before the flames took hold.
🚀 The Universe Runs on Quantum Information (Like a Computer 💻🌌)
https://lnkd.in/geGmy856
What if the universe didn’t start with matter or space…
but with information?
Not particles.
Not time.
Not gravity.
Just pure quantum information, like a computer that’s powered on but hasn’t loaded anything yet.
A new computational model of the brain based closely on its biology and physiology not only learned a simple visual category learning task exactly as well as lab animals, but even enabled the discovery of counterintuitive activity by a group of neurons that researchers working with animals to perform the same task had not noticed in their data before, says a team of scientists at Dartmouth College, MIT, and the State University of New York at Stony Brook.
Notably, the model produced these achievements without ever being trained on any data from animal experiments. Instead, it was built from scratch to faithfully represent how neurons connect into circuits and then communicate electrically and chemically across broader brain regions to produce cognition and behavior. Then, when the research team asked the model to perform the same task that they had previously performed with the animals (looking at patterns of dots and deciding which of two broader categories they fit), it produced highly similar neural activity and behavioral results, acquiring the skill with almost exactly the same erratic progress.
“It’s just producing new simulated plots of brain activity that then only afterward are being compared to the lab animals. The fact that they match up as strikingly as they do is kind of shocking,” says Richard Granger, a professor of psychological and brain sciences at Dartmouth and senior author of a new study in Nature Communications that describes the model.
Quantum computers, systems that process information leveraging quantum mechanical effects, are expected to outperform classical computers on some complex tasks. Over the past few decades, many physicists and quantum engineers have tried to demonstrate the advantages of quantum systems over their classical counterparts on specific types of computations.
Researchers at Autonomous University of Barcelona and Hunter College of CUNY recently showed that quantum systems could tackle a problem that cannot be solved by classical systems, namely determining the even or odd nature of particle permutations without marking all and each one of the particles with a distinct label. This task essentially entails uncovering whether re-arranging particles from their original order to a new order requires an even or odd number of swaps in the position of particle pairs.
These researchers have been conducting research focusing on problems that entail the discrimination between quantum states for several years. Their recent paper, published in Physical Review Letters, demonstrates that quantum technologies could solve one of these problems in ways that are unfeasible for classical systems.
“We saw some peculiar brain activity in the model,” Miller says. “There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn’t a quirk of the model — the monkeys’ brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.”
The new work, published in Nature Communications, puts a name to these overlooked signals: incongruent neurons, or ICNs, and explores theories as to why a primate brain might want to keep alternate options in mind, even if they’re not the right ones at the moment.
Beyond identifying a previously unrecognized class of neurons involved in learning, the study shows that the model behaves like a brain and generates realistic brain activity, even without being trained on neural data. The findings could have major implications for testing potential neurological drugs and for using computational models to investigate how cognition emerges and functions.
A new transceiver invented by electrical engineers at the University of California, Irvine boosts radio frequencies into 140-gigahertz territory, unlocking data speeds that rival those of physical fiber-optic cables and laying the groundwork for a transition to 6G and FutureG data transmission protocols.
To create the transceiver, researchers in UC Irvine’s Samueli School of Engineering devised a unique architecture that blends digital and analog processing. The result is a silicon chip system, comprising both a transmitter and a receiver, that’s capable of processing digital signals significantly faster and with much greater energy efficiency than previously available technologies.
The team from UC Irvine’s Nanoscale Communication Integrated Circuits Labs outline its work in two papers published this month in the IEEE Journal of Solid-State Circuits. In one, the engineers discuss the technology they call a “bits-to-antenna” transmitter, and in the second, they cover their “antenna-to-bits” receiver.