Recent backscatter communication techniques enable ultra low power wireless devices that operate without batteries while interoperating directly with unmodified commodity wireless devices. Commodity devices cooperate in providing the unmodulated carrier that the battery-free nodes need to communicate while collecting energy from their environment to perform sensing, computation, and communication tasks. The optimal provision of the unmodulated carrier limits the size of the network because it is an NP-hard combinatorial optimization problem. Consequently, previous works either ignore carrier optimization altogether or resort to suboptimal heuristics, wasting valuable energy and spectral resources. We present DeepGANTT, a deep learning scheduler for battery-free devices interoperating with wireless commodity ones. DeepGANTT leverages graph neural networks to overcome variable input and output size challenges inherent to this problem. We train our deep learning scheduler with optimal schedules of relatively small size obtained from a constraint optimization solver. DeepGANTT not only outperforms a carefully crafted heuristic solution but also performs within ~3% of the optimal scheduler on trained problem sizes. Finally, DeepGANTT generalizes to problems more than four times larger than the maximum used for training, therefore breaking the scalability limitations of the optimal scheduler and paving the way for more efficient backscatter networks.

Coco, the robot based delivery service, announced the official launch of COCO 1, a larger, more advanced version of its signature pink bot. The COCO 1 is a first of its kind delivery robot designed and manufactured in partnership with the largest micro mobility hardware manufacturer, Segway. Coco is currently deploying 1,000s of COCO 1 robots to serve local merchants in multiple cities, over the next few months. With its increased carrying capacity, the COCO 1 will deliver larger orders for a wider range of merchants, further eliminating the need for car-based delivery. Compared to the current model, the COCO 1 offers a number of added features including a more efficient drivetrain and a larger battery capacity that allows for an increased delivery radius of up to three miles, nearly double the radius of the original model.

To discover materials for better batteries, researchers must wade through a vast field of candidates. New research demonstrates a machine learning technique that could more quickly surface ones with the most desirable properties. The study could accelerate designs for solid-state batteries, a promising next-generation technology that has the potential to store more energy than lithium-ion batteries without the flammability concerns. However, solid-state batteries encounter problems when materials within the cell interact with each other in ways that degrade performance. Researchers from the National Renewable Energy Laboratory (NREL), the Colorado School of Mines, and the University of Illinois demonstrated a machine learning method that can accurately predict the properties of inorganic compounds.

In this work, we introduce a novel workflow that couples robotics to machine-learning for efficient optimization of a non-aqueous battery electrolyte. A custom-built automated experiment named "Clio" is coupled to Dragonfly - a Bayesian optimization-based experiment planner. Clio autonomously optimizes electrolyte conductivity over a single-salt, ternary solvent design space. Using this workflow, we identify 6 fast-charging electrolytes in 2 work-days and 42 experiments (compared with 60 days using exhaustive search of the 1000 possible candidates, or 6 days assuming only 10% of candidates are evaluated). Our method finds the highest reported conductivity electrolyte in a design space heavily explored by previous literature, converging on a high-conductivity mixture that demonstrates subtle electrolyte chemical physics.

Energy systems optimization problems are complex due to strongly non-linear system behavior and multiple competing objectives, e.g. economic gain vs. environmental impact. Moreover, a large number of input variables and different variable types, e.g. continuous and categorical, are challenges commonly present in real-world applications. In some cases, proposed optimal solutions need to obey explicit input constraints related to physical properties or safety-critical operating conditions. This paper proposes a novel data-driven strategy using tree ensembles for constrained multi-objective optimization of black-box problems with heterogeneous variable spaces for which underlying system dynamics are either too complex to model or unknown. In an extensive case study comprised of synthetic benchmarks and relevant energy applications we demonstrate the competitive performance and sampling efficiency of the proposed algorithm compared to other state-of-the-art tools, making it a useful all-in-one solution for real-world applications with limited evaluation budgets.

In 2018, Christian Engler felt he'd studied enough theory at the ETH Zurich and longed to put it all into practice. It was evident to Christian that the best way to get hands-on experience was to start something himself. Others were not so sure. Especially when they heard about his ambition to revive a project from high school. The project involved underwater robots, also known as Remotely Operated Vehicles (ROVs).

Advancing lithium-ion batteries (LIBs) in both design and usage is key to promoting electrification in the coming decades to mitigate human-caused climate change. Inadequate understanding of LIB degradation is an important bottleneck that limits battery durability and safety. Here, we propose hybrid physics-based and data-driven modeling for online diagnosis and prognosis of battery degradation. Compared to existing battery modeling efforts, we aim to build a model with physics as its backbone and statistical learning techniques as enhancements. Such a hybrid model has better generalizability and interpretability together with a well-calibrated uncertainty associated with its prediction, rendering it more valuable and relevant to safety-critical applications under realistic usage scenarios.

Discovering meaningful conceptual structures is a substantial task in data mining and knowledge discovery applications. While off-the-shelf interestingness indices defined in Formal Concept Analysis may provide an effective relevance evaluation in several situations, they frequently give inadequate results when faced with massive formal contexts (and concept lattices), and in the presence of irrelevant concepts. In this paper, we introduce the Conceptual Relevance (CR) score, a new scalable interestingness measurement for the identification of actionable concepts. From a conceptual perspective, the minimal generators provide key information about their associated concept intent. Furthermore, the relevant attributes of a concept are those that maintain the satisfaction of its closure condition. Thus, the guiding idea of CR exploits the fact that minimal generators and relevant attributes can be efficiently used to assess concept relevance. As such, the CR index quantifies both the amount of conceptually relevant attributes and the number of the minimal generators per concept intent. Our experiments on synthetic and real-world datasets show the efficiency of this measure over the well-known stability index.

An exoskeleton that lets amputees feel like they are'walking with two normal legs' has been developed by scientists using battery-powered electric motors. The powerful exoskeleton, which wraps around the wearer's waist and leg, was developed by a team of engineers at the University of Utah in Salt Lake City. It has been designed for above-the-knee amputees and uses battery-powered electric motors and embedded microprocessors to reduce walking effort. The 5.4lb frame is made of carbon-fibre material, plastic composites and aluminium and can walk for miles between charges, according to its creators. Those wearing it saw a 15.6 per cent reduction in their metabolic rate, equivalent to taking off a 26-pound backpack while out on a long walk, the team said.

OceanX, a team of marine biologists, media and filmmakers, embarked on a quest in 2020 to explore the depths of the Red Sea where they not only found a giant shipwreck, but a massive creature that appeared to be larger than a human. While investigating the'Pella,' which sank in November 2011, at a depth of 2,800 feet, the group spotted what they thought could be'The Giant Squid.' 'I will never forget what happened next for as long as I live,' said OceanX science program lead Mattie Rodrigue in a video taken of the discovery. 'All of a sudden, as we're looking at the bow of the shipwreck, this massive creature comes into view, takes a look at the ROV [remotely operated vehicle] and curls its entire body around the bow of the wreck.' It was not until September 2021 did the team learn that the mysterious creature was'the giant form' of the purpleback flying squid, which typically grow up to two feet long. The OceanX team traveled to the Red Sea aboard the OceanXplorer, a research vessel with a 40-ton crane to launch submersibles, towed sonar arrays and other heavy equipment down into the depths.