Lifeboat Foundation

Large-scale supercomputer simulations at the atomic level show that the dominant G form variant of the COVID-19-causing virus is more infectious partly because of its greater ability to readily bind to its target host receptor in the body, compared to other variants. These research results from a Los Alamos National Laboratory-led team illuminate the mechanism of both infection by the G form and antibody resistance against it, which could help in future vaccine development.

“We found that the interactions among the basic building blocks of the Spike protein become more symmetrical in the G form, and that gives it more opportunities to bind to the receptors in the host—in us,” said Gnana Gnanakaran, corresponding author of the paper published today in Science Advances. “But at the same time, that means antibodies can more easily neutralize it. In essence, the variant puts its head up to bind to the receptor, which gives antibodies the chance to attack it.”

Researchers knew that the variant, also known as D614G, was more infectious and could be neutralized by antibodies, but they didn’t know how. Simulating more than a million individual atoms and requiring about 24 million CPU hours of supercomputer time, the new work provides molecular-level detail about the behavior of this variant’s Spike.

Composite membrane origami has been an efficient and effective method for constructing transformable mechanisms while considerably simplifying their design, fabrication, and assembly; however, its limited load-bearing capability has restricted its application potential. With respect to wheel design, membrane origami offers unique benefits compared with its conventional counterparts, such as simple fabrication, high weight-to-payload ratio, and large shape variation, enabling softness and flexibility in a kinematic mechanism that neutralizes joint distortion and absorbs shocks from the ground. Here, we report a transformable wheel based on membrane origami capable of bearing more than a 10-kilonewton load. To achieve a high payload, we adopt a thick membrane as an essential element and introduce a wireframe design rule for thick membrane accommodation. An increase in the thickness can cause a geometric conflict for the facet and the membrane, but the excessive strain energy accumulation is unique to the thickness increase of the membrane. Thus, the design rules for accommodating membrane thickness aim to address both geometric and physical characteristics, and these rules are applied to basic origami patterns to obtain the desired wheel shapes and transformation. The capability of the resulting wheel applied to a passenger vehicle and validated through a field test. Our study shows that membrane origami can be used for high-payload applications.

Origami has been a rich source of inspiration for art, education, and mathematics, and it has proven to be an efficient and effective method for realizing transformable structures in nature (1–3) and artificial systems (4–8). Composite membrane origami, the design technique based on the laminar composition of flexible membranes with rigid facet constraints, opens a new field for robotics by the transition from component assembly to lamination, which considerably simplifies design, fabrication, and assembly. This transition simplifies and speeds up fabrication and enables reaching size scales that were difficult to access before (9, 10). In addition, membrane origami provides a versatile shape-changing ability that has been exploited in various applications (11–15), and its applicability has been extended by additional design dimensions obtained from material characteristics such as softness and stretchability (16–19).

Beyond the aforementioned benefits, origami has been an effective design tool for constructing a high payload-to-weight structure, such as a honeycomb panel, by markedly increasing the buckling strength using unique geometric configurations (20, 21). Combining this feature with reconfigurability, various stiffness transition mechanisms have also been introduced (22–24). The rigidity of components is another important factor to secure high load capacity and closely related to the thickness. Origami design is, traditionally, a matter of organizing fold lines under fundamental and ideal assumptions—zero facet thickness and zero fold line width (25–27). However, in response to growing interest in origami-inspired applications that require load-bearing capability, various thickness accommodation methods have been introduced (28–30).

Hydrogels are an exciting class of materials for new and emerging robotics. For example, actuators based on hydrogels have impressive deformability and responsiveness. Studies into hydrogels with autonomous locomotive abilities, however, are limited. Existing hydrogels achieve locomotion through the application of cyclical stimuli or chemical modifications. Here, we report the fabrication of active hydrogels with an intrinsic ability to move on the surface of water without operated stimuli for up to 3.5 hours. The active hydrogels were composed of hydrophobic and hydrophilic groups and underwent a dynamic wetting process to achieve spatial and temporal control of surface tension asymmetry. Using surface tension, the homogeneous active hydrogels propelled themselves and showed controlled locomotion on water, similar to common water striders.

Continuous and controlled shape morphing is essential for soft machines to conform, grasp, and move while interacting safely with their surroundings. Shape morphing can be achieved with two-dimensional (2D) sheets that reconfigure into target 3D geometries, for example, using stimuli-responsive materials. However, most existing solutions lack the ability to reprogram their shape, face limitations on attainable geometries, or have insufficient mechanical stiffness to manipulate objects. Here, we develop a soft, robotic surface that allows for large, reprogrammable, and pliable shape morphing into smooth 3D geometries. The robotic surface consists of a layered design composed of two active networks serving as artificial muscles, one passive network serving as a skeleton, and cover scales serving as an artificial skin.

Cybersecurity specialists report that a hacker is selling real-time access to a single-use password system, allowing cybercriminals to access Facebook, Twitter, Google, Amazon, Microsoft, Signal, Telegram accounts, among many others without having to obtain multi-factor authentication codes.

This report should be taken seriously, as a related attack could engage billions of users. In turn, cybersecurity experts point out that this is the consequence of using servers that handle OTP requests from online service users.

The first reports on this hacker were published by researcher Rajshekhar Rajaharia, who mentions that the hacker offers 50 GB of data extracted from multiple sources and webshell access to the OTP generating platform. The seller asks for about $5000 USD in cryptocurrency, although Rajaharia notes that initially the hacker planned to sell this information for about $18000 USD.

KAIST researchers have developed a novel nanofiber production technique called ‘centrifugal multispinning’ that will open the door for the safe and cost-effective mass production of high-performance polymer nanofibers. This new technique, which has shown up to a 300 times higher nanofiber production rate per hour than that of the conventional electrospinning method, has many potential applications including the development of face mask filters for coronavirus protection.

Nanofibers make good face mask filters because their mechanical interactions with aerosol particles give them a greater ability to capture more than 90% of harmful particles such as fine dust and virus-containing droplets.

The impact of the COVID-19 pandemic has further accelerated the growing demand in recent years for a better kind of face mask. A polymer nanofiber-based mask filter that can more effectively block harmful particles has also been in higher demand as the pandemic continues.

Encoding information into light, and transmitting it through optical fibers lies at the core of optical communications. With an incredibly low loss of 0.2 dB/km, optical fibers made from silica have laid the foundations of today’s global telecommunication networks and our information society.

Such ultralow optical loss is equally essential for integrated photonics, which enable the synthesis, processing and detection of optical signals using on-chip waveguides. Today, a number of innovative technologies are based on integrated photonics, including semiconductor lasers, modulators, and photodetectors, and are used extensively in data centers, communications, sensing and computing.

Integrated photonic chips are usually made from silicon that is abundant and has good optical properties. But silicon can’t do everything we need in integrated photonics, so new material platforms have emerged. One of these is silicon nitride (Si3N4), whose exceptionally low optical loss (orders of magnitude lower than that of silicon), has made it the material of choice for applications for which low loss is critical, such as narrow-linewidth lasers, photonic delay lines, and nonlinear photonics.

Spin waves could unlock the next generation of computer technology, a new component allows physicists to control them.

Researchers at Aalto University have developed a new device for spintronics. The results have been published in the journal Nature Communications, and mark a step towards the goal of using spintronics to make computer chips and devices for data processing and communication technology that are small and powerful.

Traditional electronics uses electrical charge to carry out computations that power most of our day-to-day technology. However, engineers are unable to make electronics do calculations faster, as moving charge creates heat, and we’re at the limits of how small and fast chips can get before overheating. Because electronics can’t be made smaller, there are concerns that computers won’t be able to get more powerful and cheaper at the same rate they have been for the past 7 decades. This is where spintronics comes in.

Nobel laureate in physics Richard Feynman once described turbulence as “the most important unsolved problem of classical physics.”

Understanding turbulence in classical fluids like water and air is difficult partly because of the challenge in identifying the vortices swirling within those fluids. Locating vortex tubes and tracking their motion could greatly simplify the modeling of turbulence.

But that challenge is easier in quantum fluids, which exist at low enough temperatures that quantum mechanics — which deals with physics on the scale of atoms or subatomic particles — govern their behavior.