Researchers in Australia are working on a way to lower the cost of producing solar thermal energy by as much as 40% with the help of shatterproof rear-view mirrors originally designed for cars.
That could be huge for agriculture and industrial facilities which need large amounts of heat for large-scale processes at temperatures between 212 — 754 °F (100 — 400 °C). That addresses food production, drying crops, grain and pulse drying, sterilizing soil and treating wastewater on farms; industrial applications include producing chemicals, making paper, desalinating water, and dyeing textiles.
A quick refresher in case you’re out of the loop: solar thermal energy and conventional solar energy (photovoltaic) systems both harvest sunlight, but they work in fundamentally different ways. Solar thermal setups capture the Sun’s heat rather than its light, use reflectors to concentrate sunlight onto a receiver, and convert solar radiation directly into heat energy. This heat can be used directly for heating buildings, water, or the aforementioned industrial processes.
Elon Musk recently emphasized that Colossus 2 will be the first Gigawatt AI training supercluster, highlighting xAI’s growing infrastructure ambitions as he reshared a post detailing the deployment of 168 Tesla Inc. Megapacks to power the new data center.
The qualia problem of perception is simply pointing out that the way we perceive the world is in terms of subjective qualities rather than numerical quantities. For example, we perceive the color of light in the things we see rather than the frequency of light wave vibrations or wavelengths, just as we perceive the quality of the sounds we hear rather than the frequency of sound wave vibrations. Another example is emotional qualities, like the perception of pleasure and pain and the perception of other emotional qualities, like the emotional qualities that color the perception of the emotional body feelings we perceive with emotional expressions of fear and desire. There is no possible way to understand the perception of these emotional qualities, just as there is no way to understand the perception of the colors we see or the qualities of the sounds we hear, in terms of the neuronal firing rates of neurons in the brain or other nervous systems. The frequency of wave vibrations and the neuronal firing rates of neurons are both examples of quantities. The problem is we do not perceive things in terms of numerical quantities, but rather in terms of subjective qualities.
All our physical theories are formulated in terms of numerical quantities, not in terms of subjective qualities. For example, in ordinary quantum theory or in quantum field theory, we speak of the frequency of light wave vibrations or the wavelength of a light wave in terms of a quantum particle called the photon. A photon or light wave is characterized by the numerical quantities of frequency and wavelength. When we formulate the nature of a light wave or photon in quantum theory in terms of Maxwell’s equations for the electromagnetic field, we can only describe numerical quantities. In ordinary quantum theory and quantum field theory, the electromagnetic field is the quantum wave-function, ψ(x, t), that specifies the quantum probability that the point particle called the photon can be measured at a position x in space at a moment t in time. That quantum probability is specified in terms of the frequency and wavelength that characterizes the wave-function for the photon.
A research team has uncovered a molecular “switch” that precisely regulates NMDA receptors, key players in brain communication, by blocking excitatory synapse overactivity.
Neural data analysis algorithms capable of tracking neuronal signals from one-photon functional imaging data longitudinally and reliably are still lacking. Here authors developed CaliAli, a tool for extracting calcium signals across multiple days. Validated with optogenetic tagging, dual-color imaging, and place cell data, CaliAli demonstrated stable neuron tracking for up to 99 days.
Researchers have developed an extremely thin, flexible imager that could be useful for noninvasively acquiring images from inside the body. The new technology could one day enable early and precise disease detection, providing critical insights to guide timely and effective treatment.
“As opposed to existing prohibitively large endoscopes made of cameras and optical lenses or bulky fiber optic bundles, our microimager is very compact,” said research team leader Maysam Chamanzar from Carnegie Mellon University. “Much thinner than a typical eyelash, our device is ideal for reaching deep regions of the body without causing significant damage to the tissue.”
In the journal Biomedical Optics Express, the researchers showed that the microimager, which is only 7 microns thick—a tenth of an eyelash diameter—and about 10 mm long, can be used in a mouse brain for structural and functional imaging of brain activity. The width of the thin film imager can be customized based on the desired field of view and resolution.
Astrobee is a free-flying robotic system developed by NASA that is made up of three distinct cube-shaped robots. This system was originally designed to help astronauts who are working at the International Space Station (ISS) by automating some of their routine manual tasks.
While Astrobee could be highly valuable for astronauts, boosting the efficiency with which they complete day-to-day operations, its object manipulation capabilities are not yet optimal. Specifically, past experiments suggest that the robot struggles when handling deformable items, including cargo bags that resemble some of those that it might be tasked to pick up on the ISS.
Researchers at Stanford University, University of Cambridge and NASA Ames recently developed Pyastrobee, a simulation environment and control stack to train Astrobee in Python, with a particular emphasis on the manipulation and transport of cargo.