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.

Oysters are the living vacuum cleaners of the oceans. There is an exponential decline in the oyster population due to over-harvesting. With the current development of the automation and AI, robots are becoming an integral part of the environmental monitoring process that can be also utilized for oyster reef preservation. Nevertheless, the underwater environment poses many difficulties, both from the practical - dangerous and time consuming operations, and the technical perspectives - distorted perception and unreliable navigation. To this end, we present a simulated environment that can be used to improve oyster reef monitoring. The simulated environment can be used to create photo-realistic image datasets with multiple sensor data and ground truth location of a remotely operated vehicle(ROV). Currently, there are no photo-realistic image datasets for oyster reef monitoring. Thus, we want to provide a new benchmark suite to the underwater community.

In recent years, significant progress has been made in transportation electrification. And lithium-ion batteries (LIB), as the main energy storage devices, have received widespread attention. Accurately predicting the state of health (SOH) can not only ease the anxiety of users about the battery life but also provide important information for the management of the battery. This paper presents a prediction method for SOH based on Vision Transformer (ViT) model. First, discrete charging data of a predefined voltage range is used as an input data matrix. Then, the cycle features of the battery are captured by the ViT which can obtain the global features, and the SOH is obtained by combining the cycle features with the full connection (FC) layer. At the same time, transfer learning (TL) is introduced, and the prediction model based on source task battery training is further fine-tuned according to the early cycle data of the target task battery to provide an accurate prediction. Experiments show that our method can obtain better feature expression compared with existing deep learning methods so that better prediction effect and transfer effect can be achieved.

Lithium-ion batteries have become a key component in the rise of electric mobility, but forecasting their health and lifespans is limiting the technology. While they've proven successful, the capacity of lithium-ion batteries degrades over time, and not just because of the ageing process that occurs during charging and discharging -- known as "cycling ageing." Lithium-ion battery cells also suffer degradation from so-called "calendar ageing," which occurs during storage, or simply when the battery is not in use. It's determined by three main factors: the rest state of charge (SOC), the rest temperature, and the duration of the rest time of a battery. Given that an electric vehicle will spend most of its life parked, predicting the cells' capacity degradation from calendar ageing is crucial; it can prolong battery life and pave the way for mechanisms that could even circumvent the phenomenon.

Whether you are in one of the more modern cars, trucks and SUVs that already come USB-ready or you're driving an older model, taking your larger devices on the go means you still need a power inverter. Your car's USB port is perfectly capable of charging your phone, but but your camera, laptop, drone, power tool or other larger device is going to need more power. And for big battery power away from home, a car power inverter provides the wattage you need, where and when you need it, for the job at hand. In-car power inverters come in various shapes and sizes with a variety of uses beyond just charging gadgets. An inverter can power a game console to keep the kids entertained on a long road trip.

As a significant ingredient regarding health status, data-driven state-of-health (SOH) estimation has become dominant for lithium-ion batteries (LiBs). To handle data discrepancy across batteries, current SOH estimation models engage in transfer learning (TL), which reserves apriori knowledge gained through reusing partial structures of the offline trained model. However, multiple degradation patterns of a complete life cycle of a battery make it challenging to pursue TL. The concept of the stage is introduced to describe the collection of continuous cycles that present a similar degradation pattern. A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps. First, with identified stage information, raw cycling data from the source battery are reconstructed into the phase space with high dimensions, exploring hidden dynamics with limited sensors. Next, domain invariant representation across cycles in each stage is proposed through cycling discrepancy subspace with reconstructed data. Third, considering the unbalanced discharge cycles among different stages, a switching estimation strategy composed of a lightweight model with the long short-term memory network and a powerful model with the proposed temporal capsule network is proposed to boost estimation accuracy. Lastly, an updating scheme compensates for estimation errors when the cycling consistency of target batteries drifts. The proposed method outperforms its competitive algorithms in various transfer tasks for a run-to-failure benchmark with three batteries. Especially through transferring the estimation model from batteries B7 to B6, the proposed method improves the estimation accuracy by as high as 42.6% in the third stage in terms of the root mean square error, compared to other state-of-the-art approaches.