Lifeboat Foundation

Thorium may sound like something out of a Marvel comic book, but the radioactive metal could provide a very real, renewable energy source.

Chinese scientists have been working on a molten salt nuclear power plant using thorium for years. They even created a prototype reactor in 2021, according to the International Atomic Energy Agency.

Continue reading “China prepares to change world with introduction of revolutionary nuclear power station: ‘This design significantly reduces the chances of meltdowns’” »

Like a supersonic jet being blasted with high-speed winds, Earth is constantly being bombarded by a stream of charged particles from the sun known as solar wind.

Just like wind around a jet or water around a boat, these solar wind streams curve around Earth’s magnetic field, or magnetosphere, forming on the sunward side of the magnetosphere a front called a bow shock and stretching it into a wind sock shape with a long tail on the nightside.

Dramatic changes to the solar wind alter the structure and dynamics of the magnetosphere. An example of such changes provides a glimpse into the behavior of other bodies in space, such as Jupiter’s moons and extrasolar planets.

The wing dynamics of flying animal species have been the inspiration for numerous flying robotic systems. While birds and bats typically flap their wings using the force produced by their pectoral and wing muscles, the processes underlying the wing movements of many insects remain poorly understood.

Researchers at Ecole Polytechnique Fédérale de Lausanne (EPFL, Switzerland) and Konkuk University (South Korea) recently set out to explore how herbivorous insects known as rhinoceros beetles deploy and retract their wings. The insight they gathered, outlined in a paper published in Nature, was then used to develop a new flapping microrobot that can passively deploy and retract its wings, without the need for extensive actuators.

“Insects, including beetles, are theoretically believed to use thoracic muscles to actively deploy and retract their wings at the wing bases, similarly to birds and bats,” Hoang-Vu Phan, the lead author of the paper, told Tech Xplore. “However, methods of recording or monitoring muscular activity still cannot determine which muscles beetles use to deploy and retract their wings nor explain how they do so.”

A novel, water-resistant patch-wearable cardioverter-defibrillator (P-WCD) is safe and effective for patients at risk for sudden cardiac arrest, according to a study published in the Aug. 6 issue of the Journal of the American College of Cardiology.

John Hummel, M.D., from The Ohio State University in Columbus, and colleagues assessed the safety and clinical effectiveness of a novel P-WCD. The analysis included 290 patients at risk for sudden cardiac arrest due to ventricular tachycardia/ventricular fibrillation who were not candidates for or refused an implantable defibrillator.

The researchers found that the clinically significant cutaneous adverse device effect rate was 2.30 percent, with no severe adverse effects. There were no device-related deaths or serious adverse events reported. The inappropriate shock rate was 0.36 per 100 patient-months. Nine patients received 11 shocks, of which nine shocks were adjudicated to be appropriate. Eight of nine appropriate shocks were successful with a single shock. Median wear time compliance was 23.5 hours per day.

First patient in UK gets dose of jab designed to kill most common form of lung cancer – and stop it coming back.

Brain-machine interfaces (BMIs) have emerged as a promising solution for restoring communication and control to individuals with severe motor impairments. Traditionally, these systems have been bulky, power-intensive, and limited in their practical applications. Researchers at EPFL have developed the first high-performance, Miniaturized Brain-Machine Interface (MiBMI), offering an extremely small, low-power, highly accurate, and versatile solution.

Published in the latest issue of the IEEE Journal of Solid-State Circuits (“MiBMI: A 192/512-Channel 2.46mm 2 Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes”) and presented at the International Solid-State Circuits Conference, the MiBMI not only enhances the efficiency and scalability of brain-machine interfaces but also paves the way for practical, fully implantable devices. This technology holds the potential to significantly improve the quality of life for patients with conditions such as amyotrophic lateral sclerosis (ALS) and spinal cord injuries.

An image of the chip. (Image: EPFL)

When Sam Payne reviewed a paper in March for Elsevier’s BioSystems, he didn’t expect to come across a figure he had created in his research. He quickly scrolled through the rest of the paper to find more figures, all copied from his work.

“It’s so blatant,” Payne, an associate professor of biology at Brigham Young University in Provo, Utah, posted on X.

Although the journal rejected the paper at Payne’s recommendation, he worried the authors would try to publish elsewhere.

Sensors that can be easily and safely introduced in the brain could have important medical applications and could also contribute to the development of brain-interfacing devices. While significant progress has been made toward the development of these sensors, most existing devices can only be deployed via invasive surgical procedures that can have numerous complications.

Researchers at Seoul National University and other institutes in South Korea recently created a new biodegradable and self-deployable tent electrode that could be far easier to insert onto the surface of the human brain. Their proposed electrode design, outlined in Nature Electronics, could naturally degrade inside the human body without leaving any residues, which means that once it is inserted in the body it does not need to be surgically removed.

“Our recent paper was born out of a growing awareness of the clinical challenges linked to the implantation of electrodes via invasive brain surgery,” Seung-Kyun Kang, corresponding author of the paper, told Medical Xpress.

Over the past couple of decades, computer scientists have developed a wide range of deep neural networks (DNNs) designed to tackle various real-world tasks. While some of these models have proved to be highly effective, some studies found that they can be unfair, meaning that their performance may vary based on the data they were trained on and even the hardware platforms they were deployed on.

For instance, some studies showed that commercially available deep learning–based tools for facial recognition were significantly better at recognizing the features of fair-skinned individuals compared to dark-skinned individuals. These observed variations in the performance of AI, in great part due to disparities in the training data available, have inspired efforts aimed at improving the fairness of existing models.

Researchers at University of Notre Dame recently set out to investigate how hardware systems can contribute to the fairness of AI. Their paper, published in Nature Electronics, identifies ways in which emerging hardware designs, such as computing-in-memory (CiM) devices, can affect the fairness of DNNs.