This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by LSEG .

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? A Ukrainian drone attack killed three people and injured 16 near the town of Foros on the Crimean Peninsula, the Russian-appointed head of Crimea, Sergei Aksyonov, wrote in a post on Telegram. Russia's Ministry of Defence said the attack occurred "using strike drones equipped with high-explosive payloads", in a resort area "where there are no military targets whatsoever".

Abstract--Bringing AI to vehicles and enabling them as sensing platforms is key to transforming maintenance from reactive to proactive. Now is the time to integrate AI copilots that speak both languages: machine and driver. This article offers a conceptual and technical perspective intended to spark interdisciplinary dialogue and guide future research and development in intelligent vehicle systems, predictive maintenance, and AI-powered user interaction. Vehicle maintenance remains largely reactive to this day, often triggered by the dreaded check engine light, sometimes at the worst possible time: in the middle of a busy week, or right before a road trip. However, today's vehicles are equipped with a dense network of sensors that can monitor nearly every aspect of performance in real time.

Concerns about the effect of greenhouse gases have motivated the development of certification protocols to quantify the industrial carbon footprint (CF). These protocols are manual, work-intensive, and expensive. All of the above have led to a shift towards automatic data-driven approaches to estimate the CF, including Machine Learning (ML) solutions. Unfortunately, the decision-making processes involved in these solutions lack transparency from the end user's point of view, who must blindly trust their outcomes compared to intelligible traditional manual approaches. In this research, manual and automatic methodologies for CF estimation were reviewed, taking into account their transparency limitations. This analysis led to the proposal of a new explainable ML solution for automatic CF calculations through bank transaction classification. Consideration should be given to the fact that no previous research has considered the explainability of bank transaction classification for this purpose. For classification, different ML models have been employed based on their promising performance in the literature, such as Support Vector Machine, Random Forest, and Recursive Neural Networks. The results obtained were in the 90 % range for accuracy, precision, and recall evaluation metrics. From their decision paths, the proposed solution estimates the CO2 emissions associated with bank transactions. The explainability methodology is based on an agnostic evaluation of the influence of the input terms extracted from the descriptions of transactions using locally interpretable models. The explainability terms were automatically validated using a similarity metric over the descriptions of the target categories. Conclusively, the explanation performance is satisfactory in terms of the proximity of the explanations to the associated activity sector descriptions.

Kurt "The Cyberguy" Knutsson speaks on the anticpation of automated gas stations that are already refueling cars in Finland. Do you find filling up your car with gas a chore? How about letting a robot do it for you? A Denmark based company called Autofuel has introduced a new robotic refueling system that can fill up your car without you ever getting out of the comfort of your front seat. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS When you sign up for the Autofuel system, you put in your car details such as make, model and license plate, what kind of fuel you want, and your payment details.

This paper considers a generalization of the Path Finding (PF) with refueling constraints referred to as the Refuelling Path Finding (RF-PF) problem. Just like PF, the RF-PF problem is defined over a graph, where vertices are gas stations with known fuel prices, and edge costs depend on the gas consumption between the corresponding vertices. RF-PF seeks a minimum-cost path from the start to the goal vertex for a robot with a limited gas tank and a limited number of refuelling stops. While RF-PF is polynomial-time solvable, it remains a challenge to quickly compute an optimal solution in practice since the robot needs to simultaneously determine the path, where to make the stops, and the amount to refuel at each stop. This paper develops a heuristic search algorithm called Refuel A* (RF-A* ) that iteratively constructs partial solution paths from the start to the goal guided by a heuristic function while leveraging dominance rules for state pruning during planning. RF-A* is guaranteed to find an optimal solution and runs more than an order of magnitude faster than the existing state of the art (a polynomial time algorithm) when tested in large city maps with hundreds of gas stations.

We present an architecture and system for understanding novel behaviors of an observed agent. The two main features of our approach are the adoption of Dennett's intentional stance and analogical reasoning as one of the main computational mechanisms for understanding unforeseen experiences. Our approach uses analogy with past experiences to construct hypothetical rationales that explain the behavior of an observed agent. Moreover, we view analogies as partial; thus multiple past experiences can be blended to analogically explain an unforeseen event, leading to greater inferential flexibility. We argue that this approach results in more meaningful explanations of observed behavior than approaches based on surface-level comparisons. A key advantage of behavior explanation over classification is the ability to i) take appropriate responses based on reasoning and ii) make non-trivial predictions that allow for the verification of the hypothesized explanation. We provide a simple use case to demonstrate novel experience understanding through analogy in a gas station environment.

Electric vehicles are key to a sustainable future for the planet, but while EVs continue their steady rise within the automotive industry, many drivers remain skeptical of making the major change. There are a number of factors behind consumer hesitancy, but one of the foremost concerns is just how long it takes to recharge a car's battery. Owners can still expect between 15 and 30 minutes to re-up their EVs for another estimated 200-300 miles, while gas stations' rates are obviously dramatically shorter--typically only a few minutes for around 400 miles. Last week, however, a team of government researchers at the Department of Energy-run Idaho National Laboratory announced extremely promising new advancements that could help the US achieve the Biden administration's lofty goal of making EVs half of all automotive sales by 2030. Thanks in part to a machine learning program analyzing vast amounts of lithium-ion battery data, scientists have reportedly found a means to safely and reliably recharge EVs' power supplies up to 90 percent within just 10 minutes.

Which three-word phrase should always be spoken cautiously? All of them, actually, but that last one -- depending on your choice of ride, a full tank of gas can now cost you within fumes-sniffing distance of a hundred bucks. How did it come to this -- again? Los Angeles is a complex place. In this weekly feature, Patt Morrison is explaining how it works, its history and its culture.

China is planning to build miles-wide'megastructures' in orbit, including solar power plants, tourism complexes, gas stations and even asteroid mining facilities. The National Natural Science Foundation of China (NSFC) announced a new five-year plan, directing researchers to develop technologies and techniques. The structures will require lightweight materials to allow larger objects to get into orbit with existing rockets. Researchers will also need to adopt technology to allow for in-orbit assembly and control. The Chinese government said there is an'urgent need' for megaprojects in space that would require ultra-large spacecraft to keep them in orbit.