This manuscript has been accepted in IEEE Transactions on Industrial Informatics. Personal use of this material is permitted. Abstract--Accurate battery capacity estimation is key to alleviating consumer concerns about battery performance and reliability of electric vehicles (EVs). However, practical data limitations imposed by stringent privacy regulations and labeled data shortages hamper the development of generalizable capacity estimation models that remain robust to real-world data distribution shifts. While self-supervised learning can leverage unlabeled data, existing techniques are not particularly designed to learn effectively from challenging field data--let alone from privacy-friendly data, which are often less feature-rich and noisier . In this work, we propose a first-of-its-kind capacity estimation model based on self-supervised pre-training, developed on a large-scale dataset of privacy-friendly charging data snippets from real-world EV operations. Our pre-training framework, snippet similarity-weighted masked input reconstruction, is designed to learn rich, generalizable representations even from less feature-rich and fragmented privacy-friendly data. Our key innovation lies in harnessing contrastive learning to first capture high-level similarities among fragmented snippets that otherwise lack meaningful context. With our snippet-wise contrastive learning and subsequent similarity-weighted masked reconstruction, we are able to learn rich representations of both granular charging patterns within individual snippets and high-level associative relationships across different snippets. Bolstered by this rich representation learning, our model consistently outperforms state-of-the-art baselines, achieving 31.9%

Rock capturing with standard excavator buckets is a challenging task typically requiring the expertise of skilled operators. Unlike soil digging, it involves manipulating large, irregular rocks in unstructured environments where complex contact interactions with granular material make model-based control impractical. Existing autonomous excavation methods focus mainly on continuous media or rely on specialized grippers, limiting their applicability to real-world construction sites. This paper introduces a fully data-driven control framework for rock capturing that eliminates the need for explicit modeling of rock or soil properties. Robustness is enhanced through extensive domain randomization of rock geometry, density, and mass, as well as the initial configurations of the bucket, rock, and goal position. To the best of our knowledge, this is the first study to develop and evaluate an RL-based controller for the rock capturing task. Experimental results show that the policy generalizes well to unseen rocks and varying soil conditions, achieving high success rates comparable to those of human participants while maintaining machine stability. Corresponding author Email address: amirmasoud.molaei@tuni.fi Keywords: Excavators, Automatic rock capturing, Reinforcement learning, High-fidelity simulation, Guiding Reward Formulation, Non-prehensile manipulation 1. Introduction Autonomous excavation holds a great promise in addressing increasing demands of the mining and construction industries, two of the largest and most essential sectors worldwide. The excavator is one of the most widely used and versatile heavy-duty mobile machines (HDMMs), which is typically operated through a hydraulic system. Excavators are utilized for a wide range of earth-moving tasks, including digging, trenching, grading, and in particular material handling. Despite their versatility, traditional manual operation of excavators can result in low efficiency, increased physical strain on operators, and exposure to hazardous environments like open-pit mines. These challenges underscore the need for automation to enhance safety and productivity. An excavator is primarily composed of three major components, the traveling body, swing body, and the front digging manipulator. The digging manipulator, includes three main parts, boom, arm, and bucket, which are actuated by hydraulic cylinders. Additionally, joints connect the swing body, boom, arm, and bucket, allowing for flexible and precise motion [1, 2, 3, 4].

Parameter-efficient fine-tuning (PEFT) of powerful pre-trained models for complex downstream tasks has proven effective in vision and language processing, yet this paradigm remains unexplored in scientific machine learning, where the objective is to model complex physical systems. We conduct the first systematic study of PEFT for pre-trained Large Operator Models (LOMs) obtained by scaling variants of Fourier Neural Operator. First, we observe that the widely used Low-Rank Adaptation (LoRA) yields markedly poorer performance on LOMs than Adapter tuning. Then, we further theoretically establish that stacked LoRA incurs a depth-amplified lower bound on approximation error within Fourier layers, whereas adapters retain universal approximation capacity and, by concentrating parameters on energy-dominant low-frequency modes, attain exponentially decaying error with bottleneck width in the Fourier domain. Motivated by the robust empirical gains of adapters and by our theoretical characterization of PDE solutions as spectrally sparse, we introduce Frequency-Adaptive Adapter (F-Adapter). F-Adapter allocates adapter capacity based on spectral complexity, assigning higher-dimension modules to low-frequency components and lower-dimension modules to high-frequency components. Our F-Adapters establish state-of-the-art (SOTA) results on multiple challenging 3D Navier-Stokes benchmarks, markedly enhancing both generalization and spectral fidelity over LoRA and other PEFT techniques commonly used in LLMs. To the best of our knowledge, this work is the first to explore PEFT for scientific machine-learning and establishes F-Adapter as an effective paradigm for this domain.

Pretrained time series models have enabled inference-only forecasting systems that produce accurate predictions without task-specific training. However, existing approaches largely focus on univariate forecasting, limiting their applicability in real-world scenarios where multivariate data and covariates play a crucial role. We present Chronos-2, a pretrained model capable of handling univariate, multivariate, and covariate-informed forecasting tasks in a zero-shot manner. Chronos-2 employs a group attention mechanism that facilitates in-context learning (ICL) through efficient information sharing across multiple time series within a group, which may represent sets of related series, variates of a multivariate series, or targets and covariates in a forecasting task. These general capabilities are achieved through training on synthetic datasets that impose diverse multivariate structures on univariate series. Chronos-2 delivers state-of-the-art performance across three comprehensive benchmarks: fev-bench, GIFT-Eval, and Chronos Benchmark II. On fev-bench, which emphasizes multivariate and covariate-informed forecasting, Chronos-2's universal ICL capabilities lead to substantial improvements over existing models. On tasks involving covariates, it consistently outperforms baselines by a wide margin. Case studies in the energy and retail domains further highlight its practical advantages. The in-context learning capabilities of Chronos-2 establish it as a general-purpose forecasting model that can be used "as is" in real-world forecasting pipelines.

State's Republican-led public service commission to decide on power expansion and prices, as Democrats vie for voice Georgia is facing the largest demand for electricity in its history, driven by nation-leading datacenter construction. The Georgia Power company has made an unprecedented bid to the agency that oversees the utility for about 10 additional gigawatts of energy in the coming years - enough to power 8.3m homes, at an estimated cost of nearly $16bn, according to the Southern Environmental Law Center . But those huge numbers are not primarily for homes or local businesses in Georgia . Instead about 80% of the company's ask is driven by datacenters, primarily for artificial intelligence, according to Tom Krause, spokesperson for the state's public service commission, or PSC. It is the largest increase ever considered by the commission in a multiyear plan and comes as the Atlanta metro area led the nation in datacenter construction last year - a phenomenon playing out across the US and increasingly sparking protests and pushback.

Countries have fallen behind on emissions goals, but Hannah Ritchie looks at the numbers and sees real gains. Get your news from a source that's not owned and controlled by oligarchs. It has been 10 years since countries signed on to the Paris Agreement, and emissions and temperatures continue to reach new highs, fueling unprecedented weather disasters around the globe. Meanwhile, the shift to clean energy is facing powerful headwinds in the United States, where climate policies are being reversed and support for clean energy is withdrawn. Yet, while the headlines paint a dismal picture of efforts to rein in climate change, the numbers often tell a different story. That is the assessment of data scientist Hannah Ritchie, a researcher at the University of Oxford and deputy editor of the publication .

Gear Home This 297-piece Kobalt Mechanics Tool Kit is just $99 at Lowe's with an included tool box This kit is typically $150, but it's just $99 at Lowe's, which makes it a fantastic gift for just about anyone. We may earn revenue from the products available on this page and participate in affiliate programs. I truly believe that a big tool kit with a dedicated carrying case is one of the best gifts you can give. It looks really impressive, it's useful for every type of person, and it's easy to wrap because it's usually rectangular (though, I recommend ditching wrapping paper this year). Right now, Lowe's has this 297-piece Mechanics Tool Set for just $99, which is a total sweet spot for gift buying.

With AI datacenters soaring power bills for households, a policy called'demand flexibility' could help ease grid strain A cheap, bipartisan tool could help the US meet increasing energy demand from AI datacenters while also easing soaring power bills for households, preventing deadly blackouts and helping the climate. The policy solution, called "demand flexibility", can be quickly deployed across the US. Demand flexibility essentially means rewarding customers for using less power during times of high demand, reducing strain on the grid or in some cases, selling energy they have captured by solar panels on their homes. Peak power demand is expected to grow by 20% over the next decade - driven by the dramatic rise of AI datacenters, onshoring of manufacturing, increasing use of EVs and growing need for air conditioning amid hotter summers. Increasing energy demand is putting states such as California and Texas at higher risk of life-threatening blackouts in extreme weather.

In this era of AI slop, the idea that generative AI tools like Midjourney and Runway could be used to make art can seem absurd. But amid all the muck, there are people using AI tools with real consideration and intent. Some of them are finding notable success as AI artists: They are gaining huge online followings, selling their work at auction, and even having it exhibited in galleries and museums. This story is from our forthcoming print issue, which is all about the body. Plus, you'll also receive a free digital report on nuclear power. Take our quiz: How much do you know about antimicrobial resistance?

As the Group of 20 leaders gather in Cape Town, clean air features on the agenda as a standalone priority for the first time in the forum's history. The reality, however, is stark. Outdoor air pollution claims 5.7 million lives each year, and a report released last week highlights the lack of international development finance for clean air. Only $3.7bn was spent globally in 2023, representing barely 1 percent of aid, with only a fraction reaching Africa. As the minister chairing the G20's environment workstream this year, I am proud to have worked with member countries and international organisations to place air pollution firmly on the agenda.