Robotic engineers have unveiled what they claim are the world's fastest shoes - footwear designed with eight wheels that increase walking speeds by 250 percent. Called Moonwalkers, they strap around your shoes and propel you forward using tiny electric motors that power weight wheels, mimicking that of roller skates. The shoes are the brainchild of a team of robotics engineers at Shift Robotics, which thought of the idea when the founder started walking to work and realized powered shoes would dramatically cut his commute by more than half. This is because it increases walking speeds from the average 3mph up to 7mph. The team markets the tech, which retails for $1,399, for those'who have the need for speed' and see's Moonwalkers as the future of walking.

Robotic engineers have unveiled what they claim are the world's fastest shoes - footwear designed with eight wheels that increase walking speeds by 250 percent. Called Moonwalkers, they strap around your shoes and propel you forward using tiny electric motors that power weight wheels, mimicking that of roller skates. The shoes are the brainchild of a team of robotics engineers at Shift Robotics, which thought of the idea when the founder started walking to work and realized powered shoes would dramatically cut his commute by more than half. This is because it increases walking speeds from the average three miles per hour up to seven miles per hour. The team markets the tech, which retails for $1,399, as for those'who have the need for speed' and see's Moonwalkers as the future of walking.

Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mechanism shares similar evolving patterns. Since batteries are complicated chemical systems, these states are highly coupled with intricate electrochemical processes. A state-coupled co-estimation method named Deep Inter and Intra-Cycle Attention Network (DIICAN) is proposed in this paper to estimate SOC, SOH, and RUL, which organizes battery measurement data into the intra-cycle and inter-cycle time scales. And to extract degradation-related features automatically and adapt to practical working conditions, the convolutional neural network is applied. The state degradation attention unit is utilized to extract the battery state evolution pattern and evaluate the battery degradation degree. To account for the influence of battery aging on the SOC estimation, the battery degradation-related state is incorporated in the SOC estimation for capacity calibration. The DIICAN method is validated on the Oxford battery dataset. The experimental results show that the proposed method can achieve SOH and RUL co-estimation with high accuracy and effectively improve SOC estimation accuracy for the whole lifespan.

Created by Jamy Herrmann at ECAL, MEMOGRAM is a (non)camera that prints our images in the form of a written description, inviting users to (re)discover those moments in images. Today, for many, the memories that remain are only those of images taken with digital cameras. This project uses many different techniques since it is both tangible and digital. Both versions are made in 3D printing and then wrapped with a paper explaining the steps of use. The electronics are comprised of a thermal printer (and a paper roll) connected to a custom PCB equipped with an Arduino nano and a bluetooth UART module.

Machine learning has been used to quickly discover some of the most promising materials for fluoride-ion batteries. The work could accelerate development of these batteries, which are tipped by some to rival, or even replace, lithium-based ones. In theory, fluoride-ion systems are ideal for batteries in everything from electric vehicles to consumer electronics. That's because fluoride ions are lightweight, small and highly stable. Fluoride is also cheaper than lithium and cobalt that are required for lithium-ion batteries.

In working with customers, we often hear of a desired Digital Twin use case to drive actionable insights through what-if scenario analysis. These use cases typically include operations efficiency management, fleet management, failure predictions, and maintenance planning, to name a few. To help customers navigate this space, we developed a concise definition and four-level Digital Twin leveling index consistent with our customers' applications. In a prior blog, we described the four-level index (shown in the figure below) to help customers understand their use cases and the technologies required to achieve their desired business value. In this blog, we will illustrate how the L4 Living Digital Twins can be used to model the behavior of a physical system whose inherent behavior evolves over time.

The onshoring and buildout of dozens of fabs, many costing tens of billions of dollars, is beginning to spill over into other areas that are critical for chip manufacturing. Materials, in particular, which often gets little attention outside of chip manufacturing, witnessed a big spike in September 2022. In fact, seven materials companies covered in this report made up more than a third of the month's total reported investments, with three of the companies garnering more than $200 million. Other investment targets were sputtering equipment and evaporation materials for deposition, high-purity polycrystalline silicon, fluorine-containing electronic gases, and silicon carbide. In the AI hardware arena, numerous startups are focusing on in-memory and near-memory compute, reducing the volume of data that needs to be moved back and forth between memory and processing elements. Novel architectures also are appearing, such as one that uses sparse mathematics.

If you're on Twitter and follow news about China, you likely have heard a pretty wild rumor recently: that President Xi Jinping was under house arrest and that there was about to be a major power grab in the country. First of all, let's be very clear: this report is false and should not be taken seriously. No credible sources on China have bought it. But it's interesting to dissect how a ridiculous rumor could be elevated and spread so widely that it made it to Twitter's deeply flawed trending list over the weekend, thanks to influencer translation and amplification from accounts based in India. This story is from China Report, MIT Technology Review's new newsletter giving you the inside scoop on what's happening in China.

Historically, researchers in materials discovery have devised and tested options through some mix of hunches, informed speculation, and trial by error. But it's a difficult and time-consuming process simply given the vast array of possible substances and combinations, which can send researchers down numerous false paths. In the case of electrolyte ingredients, "you can mix and match them in billions of ways," says Venkat Viswanathan, an associate professor at Carnegie Mellon, a co-author of the Nature Communications paper, and a cofounder and chief scientist at Aionics. He collaborated with Jay Whitacre, director of the university's Wilton E. Scott Institute for Energy Innovation and the co-principal investigator on the project, along with other Carnegie researchers to explore how robotics and machine learning could help. The promise of a system like Clio and Dragonfly is that it can rapidly work through a wider array of possibilities than human researchers can, and apply what it learns in a systematic way.

The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal-noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9 %, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.