Believe it or not, fighter jets, flying cars, natural gas pipelines and plastic bottles may be more alike than you think. They might one day be made with TuFF -- a high-performance short-fiber composite material invented at the University of Delaware that is superstrong, ultra-lightweight and virtually indestructible. It might even be the Superman of materials. Developed by researchers at UD's Center for Composite Materials as part of a Defense Advanced Projects Agency (DARPA) Defense Sciences Office program, TuFF (Tailored Universal Feedstock for Forming) has properties equal to the very best composites used in space and aerospace applications today. And, according to CCM Director Jack Gillespie, the uses for TuFF are starting to take off -- literally.

GM has released more details about its next-generation Ultium batteries, including plans for lithium-metal (Li-metal) technology to boost performance and energy density. The automaker announced that it has signed an agreement to work with SolidEnergy Systems (SES), an MIT spinoff developing prototype Li-metal batteries with nearly double the capacity of current lithium-ion cells. As a reminder, Li-metal batteries replace carbon anodes with lithium metal, allowing for lighter and more powerful cells. The challenge with the technology is increased resistance and "dendrite" filaments that tend to form on the anodes, making batteries short-circuit and heat up. Previous lithium-metal batteries would only work when heated up to 175 degrees F, but SolidEnergy developed an electrolyte coating for lithium metal foil that works at room temperature.

Laid waste by a nuclear disaster a decade ago, Fukushima Prefecture is still struggling to recover, even as the government tries to bring people and jobs back to former ghost towns by pouring in trillions of yen to decontaminate and rebuild. But reconstruction efforts, from the mundane -- supermarkets and transport infrastructure -- to a cutting-edge hydrogen energy plant, have yet to entice more than a small fraction of the former population to return. As the country marks the 10th anniversary of the March 11, 2011 earthquake, tsunami and nuclear meltdown, parts of the prefecture are still off limits, and it remains a laggard in recovery. Its future is clouded by the 30 to 40 years it may take to decommission the crippled Fukushima No. 1 nuclear plant, near which massive amounts of treated radioactive water are in storage. The town of Namie, where a stone monument lists about 200 townspeople who died in the tsunami, emptied out overnight following the accident at the nuclear plant about 8 kilometers south.

In the univariate setting, using the kernel spectral representation is an appealing approach for generating stationary covariance functions. However, performing the same task for multiple-output Gaussian processes is substantially more challenging. We demonstrate that current approaches to modelling cross-covariances with a spectral mixture kernel possess a critical blind spot. For a given pair of processes, the cross-covariance is not reproducible across the full range of permitted correlations, aside from the special case where their spectral densities are of identical shape. We present a solution to this issue by replacing the conventional Gaussian components of a spectral mixture with block components of finite bandwidth (i.e. rectangular step functions). The proposed family of kernel represents the first multi-output generalisation of the spectral mixture kernel that can approximate any stationary multi-output kernel to arbitrary precision.

Quantum machine learning (QML) offers a powerful, flexible paradigm for programming near-term quantum computers, with applications in chemistry, metrology, materials science, data science, and mathematics. Here, one trains an ansatz, in the form of a parameterized quantum circuit, to accomplish a task of interest. However, challenges have recently emerged suggesting that deep ansatzes are difficult to train, due to flat training landscapes caused by randomness or by hardware noise. This motivates our work, where we present a variable structure approach to build ansatzes for QML. Our approach, called VAns (Variable Ansatz), applies a set of rules to both grow and (crucially) remove quantum gates in an informed manner during the optimization. Consequently, VAns is ideally suited to mitigate trainability and noise-related issues by keeping the ansatz shallow. We employ VAns in the variational quantum eigensolver for condensed matter and quantum chemistry applications and also in the quantum autoencoder for data compression, showing successful results in all cases.

The biggest energy transition in history is well and truly underway, and nowhere is the shift more readily apparent than in the transport industry. Wall Street is almost unanimous that electric vehicles are the future of the industry, with EV sales already outpacing ICE sales in markets such as Norway. That kind of exponential growth can only mean one thing: Explosive demand for the metals that go into those batteries. Demand for battery metals is projected to soar as the transport industry continues to electrify at a record pace. In fact, there's a real danger that current mining technologies might struggle to keep up with the demand for battery metals in the near future. Thankfully, Artificial intelligence (AI) can not only be deployed to help improve the way these crucial elements are mined but can replace them altogether.

Africa is home to the world's fastest growing population, which is expected to double by 2050. This growth is directly linked to the increase in demand for energy – indeed the African Energy Chamber projects that the continent's demand for power will keep rising between 4-5% per year, possibly doubling by 2050. A reversal of fortune for the world's unelectrified population is one of the Sustainable Development Goals of the United Nations (SDG7). African governments have traditionally relied on centralised grid expansion to improve electricity access. This requires significant capital expenditure and is often not time or cost effective, especially in rural areas where much of Africa's unelectrified population live. At the same time, the Paris Agreement enshrines the global aim to achieve Net Zero in the next 3 decades in order to meet the goal of keeping global temperature rise well below 2 degrees Celsius above pre-industrial levels.

Many practical combustion systems such as those in rockets, gas turbines, and internal combustion engines operate under high pressures that surpass the thermodynamic critical limit of fuel-oxidizer mixtures. These conditions require the consideration of complex fluid behaviors that pose challenges for numerical simulations, casting doubts on the validity of existing subgrid-scale (SGS) models in large-eddy simulations of these systems. While data-driven methods have shown high accuracy as closure models in simulations of turbulent flames, these models are often criticized for lack of physical interpretability, wherein they provide answers but no insight into their underlying rationale. The objective of this study is to assess SGS stress models from conventional physics-driven approaches and an interpretable machine learning algorithm, i.e., the random forest regressor, in a turbulent transcritical non-premixed flame. To this end, direct numerical simulations (DNS) of transcritical liquid-oxygen/gaseous-methane (LOX/GCH4) inert and reacting flows are performed. Using this data, a priori analysis is performed on the Favre-filtered DNS data to examine the accuracy of physics-based and random forest SGS-models under these conditions. SGS stresses calculated with the gradient model show good agreement with the exact terms extracted from filtered DNS. The accuracy of the random-forest regressor decreased when physics-based constraints are applied to the feature set. Results demonstrate that random forests can perform as effectively as algebraic models when modeling subgrid stresses, only when trained on a sufficiently representative database. The employment of random forest feature importance score is shown to provide insight into discovering subgrid-scale stresses through sparse regression.

Cold winter days are the hardest to endure for people suffering energy poverty. "I was getting lethargic sitting still to keep warm," says one. I was saving up the money. Mentally, I was losing my health, cutting down on so many things," says another. "I was holding off with the laundry, even holding off going out looking for a job because you need clean clothes." These are cries for help from people who have contacted the Fuel Bank Foundation over the past year. The charity provides emergency credit to those struggling to pay their energy bills. Requests for support have increased by 23 per cent since the start of the coronavirus pandemic. Worse still, the foundation says self-disconnection, where households switch off their power supply completely, is a growing problem. Choosing between heating and eating, or between having power or going into debt, are decisions increasing numbers of people are having to make. It has been a long, tough winter. Unemployment currently stands at 5.1 ...

DEME Offshore and Sabca have carried out a series of tests at the Rentel offshore wind farm with an aim to automate critical and ad hoc operations in the near future by using autonomous aerial vehicles (AAVs) and artificial intelligence (AI). The companies, which teamed up two years ago, have performed the first commercial, cross-border, "beyond visual line of sight" (BVLOS) drone operations at the wind farm 35 kilometres off the Belgian coast, where tests in Search & Rescue operations, environmental surveys, turbine and substation inspections, as well as parcel deliveries took place. During the tests, both a multicopter drone and a fixed-wing surveillance drone with a wing span of more than 3 metres were deployed in parallel. The long endurance surveillance drone took off from the Belgian coast and flew to the Rentel offshore wind farm. Meanwhile, an automated resident drone performed inspections and cargo flights from the substation and vessels.