Lifeboat Foundation

Combining two forms of sustainable energy into one range-extending propulsion system, Swiss Sustainable Yachts’ clean, quiet catamaran promises to jumpstart a future in which the word “range” becomes obsolete. The 64-footer harnesses solar energy to create its own hydrogen, powering a fuel cell-electric drive to potentially limitless autonomy, so long as the sun is shining and the captain isn’t pushing past cruising speed. The Aquon One might prove the ultimate luxury smart yacht of the sustainable generation.

The Aquon One has a 134-hp fuel cell-powered electric engine in each hull. Swiss Sustainable Yachts (SSY) explains that it opts for hydrogen power because of its light weight as compared to batteries or fossil fuels, long-lasting storage capability and lack of harmful emissions. Also critical to the Aquon One design is hydrogen’s ability to be created sustainably, in this case using a solar-powered electrolyzer that splits hydrogen from desalinated seawater. The 689 square feet (64 sq m) of solar panels covering the Aquon One’s hard-top generate all the electricity needed to develop the hydrogen, which is then stored away in carbon tanks.

Continue reading “Swiss smart yacht points solar-hydrogen power toward ‘limitless’ range” »

For the first time in history, a spacecraft has touched the Sun. NASA’s Parker Solar Probe has now flown through the Sun’s upper atmosphere – the corona – and sampled particles and magnetic fields there.

The months-long project demonstrates the physics behind the CPUs we take for granted.

Computer chips have become so tiny and complex that it’s sometimes hard to remember that there are real physical principles behind them. They aren’t just a bunch of ever-increasing numbers. For a practical (well, virtual) example, check out the latest version of a computer processor built exclusively inside the Minecraft game engine.

Continue reading “This 8-bit processor built in Minecraft can run its own games” »

Stacking transistors could be the next big thing in chips.

IBM and Samsung have announced their latest advance in semiconductor design: a new way to stack transistors vertically on a chip (instead of lying flat on the surface of the semiconductor).

Continue reading “IBM and Samsung say their new chip design could lead to week-long battery life on phones” »

Abstract: A central goal of condensed-matter physics is to understand how the diverse electronic and optical properties of crystalline materials emerge from the wavelike motion of electrons through periodically arranged atoms. However, more than 90 years after Bloch derived the functional forms of electronic waves in crystals [1] (now known as Bloch wavefunctions), rapid scattering processes have so far prevented their direct experimental reconstruction. In high-order sideband generation [2–9], electrons and holes generated in semiconductors by a near-infrared laser are accelerated to a high kinetic energy by a strong terahertz field, and recollide to emit near-infrared sidebands before they are scattered. Here we reconstruct the Bloch wavefunctions of two types of hole in gallium arsenide at wavelengths much longer than the spacing between atoms by experimentally measuring sideband polarizations and introducing an elegant theory that ties those polarizations to quantum interference between different recollision pathways. These Bloch wavefunctions are compactly visualized on the surface of a sphere. High-order sideband generation can, in principle, be observed from any direct-gap semiconductor or insulator. We thus expect that the method introduced here can be used to reconstruct low-energy Bloch wavefunctions in many of these materials, enabling important insights into the origin and engineering of the electronic and optical properties of condensed matter.

From: Joseph Costello [view email].

Spin-orbit acoustics is determinant to provide new perspectives and functionalities for sound manipulations. Here the authors theoretically and experimentally demonstrate acoustic spin-orbit interaction enabling chiral sound-matter interactions with unprecedented applications.

Abstract. The cnidarian model organism Hydra has long been studied for its remarkable ability to regenerate its head, which is controlled by a head organizer located near the hypostome. The canonical Wnt pathway plays a central role in head organizer function during regeneration and during bud formation, which is the asexual mode of reproduction in Hydra. However, it is unclear how shared the developmental programs of head organizer genesis are in budding and regeneration. Time-series analysis of gene expression changes during head regeneration and budding revealed a set of 298 differentially expressed genes during the 48-h head regeneration and 72-h budding time courses. In order to understand the regulatory elements controlling Hydra head regeneration, we first identified 27,137 open-chromatin elements that are open in one or more sections of the organism body or regenerating tissue. We used histone modification ChIP-seq to identify 9,998 candidate proximal promoter and 3,018 candidate enhancer-like regions respectively. We show that a subset of these regulatory elements is dynamically remodeled during head regeneration and identify a set of transcription factor motifs that are enriched in the enhancer regions activated during head regeneration. Our results show that Hydra displays complex gene regulatory structures of developmentally dynamic enhancers, which suggests that the evolution of complex developmental enhancers predates the split of cnidarians and bilaterians.

Almost all animals must make decisions on the move. Here, employing an approach that integrates theory and high-throughput experiments (using state-of-the-art virtual reality), we reveal that there exist fundamental geometrical principles that result from the inherent interplay between movement and organisms’ internal representation of space. Specifically, we find that animals spontaneously reduce the world into a series of sequential binary decisions, a response that facilitates effective decision-making and is robust both to the number of options available and to context, such as whether options are static (e.g., refuges) or mobile (e.g., other animals). We present evidence that these same principles, hitherto overlooked, apply across scales of biological organization, from individual to collective decision-making.

Animal movement data have been deposited in GitHub (https://github.

Most often, we recognize deep learning as the magic behind self-driving cars and facial recognition, but what about its ability to safeguard the quality of the materials that make up these advanced devices? Professor of Materials Science and Engineering Elizabeth Holm and Ph.D. student Bo Lei have adopted computer vision methods for microstructural images that not only require a fraction of the data deep learning typically relies on but can save materials researchers an abundance of time and money.

Quality control in materials processing requires the analysis and classification of complex material microstructures. For instance, the properties of some high strength steels depend on the amount of lath-type bainite in the material. However, the process of identifying bainite in microstructural images is time-consuming and expensive as researchers must first use two types of microscopy to take a closer look and then rely on their own expertise to identify bainitic regions. “It’s not like identifying a person crossing the street when you’re driving a car,” Holm explained “It’s very difficult for humans to categorize, so we will benefit a lot from integrating a deep learning approach.”

Their approach is very similar to that of the wider computer-vision community that drives facial recognition. The model is trained on existing material microstructure images to evaluate new images and interpret their classification. While companies like Facebook and Google train their models on millions or billions of images, materials scientists rarely have access to even ten thousand images. Therefore, it was vital that Holm and Lei use a “data-frugal method,” and train their model using only 30–50 microscopy images. “It’s like learning how to read,” Holm explained. “Once you’ve learned the alphabet you can apply that knowledge to any book. We are able to be data-frugal in part because these systems have already been trained on a large database of natural images.”