Tesla says its annual revenue has fallen for the first time as the electric vehicle (EV) maker shifts it focus to artificial intelligence (AI) and robotics. The company, which is run by multi-billionaire Elon Musk, reported a 3% decline in total revenues in 2025, while profits fell 61% in the last three months of the year. Tesla also announced plans to end production of its Model S and Model X vehicles. It will now use the manufacturing plant in California that made those cars to produce its line of humanoid robots - known as Optimus. In January, China's BYD overtook Tesla as the world's biggest EV maker, while Musk's involvement in politics both in the US and abroad has proved controversial.

A simple trick cured my tinnitus after a long-haul flight left me in misery for months. Here's the miracle method I wish everyone knew I was diagnosed with cancer after strange things began happening to my hands - here are the symptoms you can't ignore Explosive twist in'diva' inmate Bryan Kohberger's life in prison revealed in the FREE The Crime Desk newsletter Marco Rubio'cocoons like a mummy' in bizarre strategy to hide naps from Trump Food Network star Valerie Bertinelli's heartbreaking struggles laid bare after confession about shock firing Devastating truth about Blind Side actor Quinton Aaron: More to this'than everyone is letting on', friends reveal... as co-star Sandra Bullock'monitors' situation Mother hit by unimaginable triple tragedy after'son, 6, fell through icy pond and brothers aged 8 and 9 jumped in to save him' Sydney Sweeney shows off her bombshell curves in racy lingerie to promote her new SYRN line - as it's revealed Hollywood Sign bra stunt could leave her facing trespassing and vandalism charges Lawyer, 44, who died on flight to London after falling asleep on her mother's shoulder had undiagnosed cardiac condition, inquest hears Top Citi banker displayed'sexually charged' behavior towards female underling and let co-workers think they were having affair, harassment lawsuit alleges Revealed: Tupac Shakur's'crack fiend mama' lived in'SCARY' houseboat community full of drug addicts like'Psycho Steve' before shock death My perfect life at $2m Manchester-by-the-Sea mansion took nasty turn when neighbors tried to ban me from getting a gun because of my HUSBAND - now I've had the last laugh Boy, 15, has been missing for two weeks after sneaking away to New York to meet stranger he'd chatted to on Roblox Nicola Peltz could barely speak Victoria Beckham's name, says interviewer who quizzed her about THAT wedding dress row in explosive new chapter of family feud Doctor who was branded'tone deaf' for flaunting her Louboutin heels at work furiously hits back at critics Used electric vehicles (EVs) now offer buyers the best value over the entire lifetime of the car, a study claims. According to experts from the University of Michigan, compared to a new mid-sized SUV with an internal combustion engine, a three-year-old EV version offers lifetime savings of £9,486 ($13,000). In comparison, buying a used petrol version would only save you £2,190 ($3,000) over the car's lifetime. However, the researchers point out that this difference is primarily driven by how fast EVs lose their value compared to other power systems.

Tesla loses place as world's top electric vehicle seller to China's BYD Tesla has lost its place as the top global seller of electric vehicles to Chinese company BYD, capping a year defined by outrage over CEO Elon Musk's political manoeuvring and the end of United States tax breaks for customers. The company revealed on Friday that it had sold 1.64 million vehicles in 2025, compared with BYD's 2.26 million vehicles. The sales represented a 9 percent decline for Tesla from a year earlier. However, the market has become increasingly crowded with competitors, with China's electric vehicle market bounding ahead. Musk's embrace of US President Donald Trump in 2024 and subsequent spearheading of a controversial "government efficiency" panel (DOGE) behind widespread layoffs of federal workers has also proved polarising.

If a major disaster like Fukushima or Chernobyl ever happens again, the world would know almost straight away, thanks to an array of government and DIY radiation-monitoring programs running globally.

The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challenging due to the complex interplay between makespan and energy consumption, particularly under practical constraints like load-dependent speed variations and battery limitations. This paper defines the multi-objective agricultural multi-electrical-robot task allocation (AMERTA) problem, which systematically incorporates these often-overlooked real-world constraints. To address this problem, we propose a hybrid hierarchical route reconstruction algorithm (HRRA) that integrates several innovative mechanisms, including a hierarchical encoding structure, a dual-phase initialization method, task sequence optimizers, and specialized route reconstruction operators. Extensive experiments on 45 test instances demonstrate HRRA's superior performance against seven state-of-the-art algorithms. Statistical analysis, including the Wilcoxon signed-rank and Friedman tests, empirically validates HRRA's competitiveness and its unique ability to explore previously inaccessible regions of the solution space. In general, this research contributes to the theoretical understanding of multi-robot coordination by offering a novel problem formulation and an effective algorithm, thereby also providing practical insights for agricultural automation.

Accurate electric vehicle (EV) charging demand forecasting is essential for stable grid operation and proactive EV participation in electricity market. Existing forecasting methods, particularly those based on graph neural networks, are often limited to modeling pairwise relationships between stations, failing to capture the complex, group-wise dynamics inherent in urban charging networks. To address this gap, we develop a novel forecasting framework namely HyperCast, leveraging the expressive power of hypergraphs to model the higher-order spatiotemporal dependencies hidden in EV charging patterns. HyperCast integrates multi-view hypergraphs, which capture both static geographical proximity and dynamic demand-based functional similarities, along with multi-timescale inputs to differentiate between recent trends and weekly periodicities. The framework employs specialized hyper-spatiotemporal blocks and tailored cross-attention mechanisms to effectively fuse information from these diverse sources: views and timescales. Extensive experiments on four public datasets demonstrate that HyperCast significantly outperforms a wide array of state-of-the-art baselines, demonstrating the effectiveness of explicitly modeling collective charging behaviors for more accurate forecasting.

Abstract--The rapid adoption of electric vehicles (EVs) introduces major challenges for decentralized charging control. Existing decentralized approaches efficiently coordinate a large number of EVs to select charging stations while reducing energy costs, preventing power peak and preserving driver privacy. These situations create competition for limited charging slots, resulting in long queues and reduced driver comfort. T o address these limitations, we propose a novel collective learning-based coordination framework that allows EVs to balance individual comfort on their selections against system-wide efficiency, i.e., the overall queues across all stations. In the framework, EVs are recommended for adaptive charging behaviors that shift priority between comfort and efficiency, achieving Pareto-optimal trade-offs under varying station capacities and dynamic spatiotemporal EV distribution. Experiments using real-world data from EVs and charging stations show that the proposed approach outperforms baseline methods, significantly reducing travel and queuing time. The results reveal that, under uncertain charging conditions, EV drivers that behave selfishly or altruistically at the right moments achieve shorter waiting time than those maintaining moderate behavior throughout. Our findings under high fractions of station outages and adversarial EVs further demonstrate improved resilience and trustworthiness of decentralized EV charging infrastructure. LECTRIC vehicles (EVs) are becoming a preferred option in intelligent transportation systems due to their energy efficiency and reduced emissions, critical in addressing environmental concerns and fuel shortages. According to recent global market reports, EV sales are projected to surpass 17 million units in 2024 (over 20% market share), with over 20 million expected in 2025 [1]. As governments expand public charging infrastructure to meet soaring demand, centralized charging management faces limitations in scalability, cost, and resilience (e.g., single points of failure) [2], [3]. A promising alternative lies in decentralized charging control among EVs. It aims to allow EVs to manage their charging based on local conditions, user preference and grid/station needs without a central authority.

The growing demand for electric vehicle (EV) charging infrastructure presents significant planning challenges, requiring efficient strategies for investment and operation to deliver cost-effective charging services. However, the potential benefits of EV charging assignment, particularly in response to varying spatial-temporal patterns of charging demand, remain under-explored in infrastructure planning. This paper proposes an integrated approach that jointly optimizes investment decisions and charging assignments while accounting for spatial-temporal demand dynamics and their interdependencies. To support efficient model development, we leverage a large language model (LLM) to assist in generating and refining the mathematical formulation from structured natural-language descriptions, significantly reducing the modeling burden. The resulting optimization model enables optimal joint decision-making for investment and operation. Additionally, we propose a distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) to address computational complexity in high-dimensional scenarios, which can be executed on standard computing platforms. We validate our approach through a case study using 1.5 million real-world travel records from Chengdu, China, demonstrating a 30% reduction in total cost compared to a baseline without EV assignment.

Multi-robot systems have emerged as a key technology for addressing the efficiency and cost challenges in labor-intensive industries. In the representative scenario of smart farming, planning efficient harvesting schedules for a fleet of electric robots presents a highly challenging frontier problem. The complexity arises not only from the need to find Pareto-optimal solutions for the conflicting objectives of makespan and transportation cost, but also from the necessity to simultaneously manage payload constraints and finite battery capacity. When robot loads are dynamically updated during planned multi-trip operations, a mandatory recharge triggered by energy constraints introduces an unscheduled load reset. This interaction creates a complex cascading effect that disrupts the entire schedule and renders traditional optimization methods ineffective. To address this challenge, this paper proposes the segment anchoring-based balancing algorithm (SABA). The core of SABA lies in the organic combination of two synergistic mechanisms: the sequential anchoring and balancing mechanism, which leverages charging decisions as `anchors' to systematically reconstruct disrupted routes, while the proportional splitting-based rebalancing mechanism is responsible for the fine-grained balancing and tuning of the final solutions' makespans. Extensive comparative experiments, conducted on a real-world case study and a suite of benchmark instances, demonstrate that SABA comprehensively outperforms 6 state-of-the-art algorithms in terms of both solution convergence and diversity. This research provides a novel theoretical perspective and an effective solution for the multi-robot task allocation problem under energy constraints.

The decarbonization of transportation relies on the widespread adoption of electric vehicles (EVs), which requires an accurate understanding of charging behavior to ensure cost-effective, grid-resilient infrastructure. Existing work is constrained by small-scale datasets, simple proximity-based modeling of temporal dependencies, and weak generalization to sites with limited operational history. To overcome these limitations, this work proposes a framework that integrates clustering with few-shot forecasting to uncover site archetypes using a novel large-scale dataset of charging demand. The results demonstrate that archetype-specific expert models outperform global baselines in forecasting demand at unseen sites. By establishing forecast performance as a basis for infrastructure segmentation, we generate actionable insights that enable operators to lower costs, optimize energy and pricing strategies, and support grid resilience critical to climate goals.