A recent study found associations between cognitive-behavioral therapy for hoarding disorder, reduced maladaptive beliefs about possessions, and changes in brain activity during discarding tasks.
MIT engineers have released DrivAerNet++, an open-source dataset of over 8,000 car designs, to accelerate automotive innovation using AI. This dataset, featuring detailed aerodynamic data, aims to enhance fuel efficiency and electric vehicle range, promoting sustainable car design advancements.
Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations before building out the most promising designs for physical testing. The details and specs of these tests, including the aerodynamics of a given car design, are typically not made public. Significant advances in performance, such as in fuel efficiency or electric vehicle range, can therefore be slow and siloed from company to company.
MIT engineers say that the search for better car designs can speed up exponentially with the use of generative artificial intelligence tools that can plow through huge amounts of data in seconds and find connections to generate a novel design. While such AI tools exist, the data they would need to learn from have not been available, at least in any sort of accessible, centralized form.
A team of roboticists at École Polytechnique Fédérale de Lausanne, working with a colleague from the University of California, has designed, built and demonstrated a bird-like robot that can launch itself into flight using spring-like legs.
The group describes their robot in a paper published in the journal Nature. Aimy Wissa, an aerospace engineer at Princeton University, has published a News & Views piece in the same journal issue suggesting possible ways the innovation could be used in real-world applications.
Some types of drones, such as those with rotors, can rise straight up off the ground—others that are powered with forward-facing rotors or engines that push exhaust out the back must either race along a runway or catapult to get airborne. For this new project, the research team developed a new design for getting such craft into the air—jumping using spring-like legs.
Transistors based on two-dimensional (2D) semiconductors, such as molybdenum disulfide (MoS2) and tungsten diselenide (WSe2), could outperform conventional silicon-based transistors, while also being easier to reduce in size. To perform well, these transistors need to be based on high-quality dielectric materials, which can be difficult to prepare.
Researchers at Nanyang Technological University, Nanjing University of Aeronautics and Astronautics recently introduced a new promising strategy to prepare the dielectric materials for these transistors. Their approach, outlined in a paper published in Nature Electronics, was successfully used to deposit an ultrathin and uniform native oxide of gallium Ga2O3 on the surface of MoS2.
“Traditional methods of preparing dielectric layer, such as atomic layer deposition (ALD), encounter quality problems because of the high-quality surface of 2D semiconductors without sufficient nucleation points, especially at thin thicknesses down to a few nanometers,” Kongyang Yi, first author of the paper, told Tech Xplore.
Researchers in China have developed an AI model that can discern species and larval stage of four lepidopteran corn pests with 96 percent accuracy.
Fitzgerald says cyborg search and rescue beetles or cockroaches might be able to help in disaster situations by finding and reporting the location of survivors and delivering lifesaving drugs to them before human rescuers can get there.
But first, the Australian researchers must master the ability to direct the movements of the insects, which could take a while. Fitzgerald says that although the work might seem futuristic now, in a few decades, cyborg insects could be saving lives.
He’s not the only roboticist creating robots from living organisms. Academics at the California Institute of Technology (Caltech), for example, are implanting electronic pacemakers into jellyfish to control their swimming speed. They hope the bionic jellies could help collect data about the ocean far below the surface.
Summary: A new study highlights how brain age models can track healthy infant development and reveal environmental influences. Using MRI data from over 600 term and preterm infants, researchers trained machine learning models to predict brain age and identify gaps between predicted and actual ages.
These brain age gaps can indicate whether an infant’s development is faster or slower than expected, with maternal age emerging as a significant influencing factor. Advanced brain development was linked to better cognitive abilities but poorer emotional regulation, suggesting that following normative developmental trajectories may be ideal.
Chinese researchers have created the BHMbot-B, a 15 mm long microrobot with quick forward and backward movements, which is ideal for navigating small places.
The robot effectively switches between forward and backward movement by aligning the vibratory motions of its magnet, cantilever, and linkages using vibration mode transition control.
The Beihnag University team claims that the device combines a battery, a control circuit for wireless operation, and two electromagnetic actuators for a high load capacity.
The quarks that make up the nuclei of all atoms around us are known to “mix”: the different types of quark occasionally change into one another. The amounts in which these processes happen are not very well known, though—and the theoretical values don’t even add up to 100%. UvA-IoP physicist Jordy de Vries and colleagues from Los Alamos, Seattle, and Bern have now published work that takes a step towards solving these mysteries.
All good things come in threes. The Standard Model of particle physics takes this motto to heart: it contains three so-called generations of elementary particles. Take the quarks as an example. In addition to the pair of quark types known as “up” and “down,” which make up the core of atomic nuclei, there exist two additional quark pairs: “charm” and “strange,” as well as “top” and “bottom.” Together, these six types of quarks are known as the six quark flavors.
The Standard Model predicts that one quark flavor can transmute into another, a phenomenon called quark mixing, but the model does not predict how often different transmutations happen. In fact, the current state-of-the-art analysis indicates that something is afoot: the probabilities of all mixings do not add up to 100%. What is going on? Could this be a signal of new physics outside of the Standard Model?