Could Iran be using China's BeiDou system? Footage shows a fire burning near Dubai International Airport after a drone ignited a fuel tank, according to authorities in the UAE. Civil defence crews say the blaze is under control. What is force majeure and why are some Gulf countries invoking it?

The tank-like vehicle is already being tested in South Korea. The robot sprays itself with water to stay cool and uses thermal cameras to see through smoke. Breakthroughs, discoveries, and DIY tips sent six days a week. Firefighters in South Korea will soon start deploying alongside a massive, six-wheeled, self-cooling autonomous robot that could help keep them safe. Hyundai recently revealed the new, driverless ground drone, built atop a chassis initially intended for military use and looking like something out of a sci-fi film.

The tree cover marks the course of the source stream, which formed the basis for the construction of the former farmstead. Breakthroughs, discoveries, and DIY tips sent six days a week. While a recent Iron Age discovery in northern Germany is proving itself an archaeological goldmine, local firefighters might be a bit annoyed by the find. According to the Regional Association of Westphalia-Lippe (LWL), construction on a new fire station in the town of Hüllhorst roughly 45 miles west of Hanover was delayed after the surveyors identified evidence of a settlement dating back over 2,500 years. As only the third such find in the region, the site offers an exceptional opportunity to learn more about ancient life in Germany prior to the Roman Empire's arrival in 1st century BCE.

V astTrack consists of a few attractive properties: (1) V ast Object Category . In particular, it covers targets from 2,115 categories, significantly surpassing object classes of existing popular benchmarks ( e.g ., GOT -10k with 563 classes and LaSOT with 70 categories). Through providing such vast object classes, we expect to learn more general object tracking.

At least three people have been killed and two others are still missing after a fire broke out at a food factory near the central Greek city of Trikala, officials say. The blaze began in the early hours of Monday at a Violanta biscuit factory, where 13 workers were on site, according to local media. Eight people managed to escape, while firefighters later recovered three bodies from the building. Drone footage showed thick smoke billowing from the fire. A powerful explosion was reportedly heard before it broke out but an investigation into the cause of the blaze is ongoing.

Things to Do in L.A. Tap to enable a layout that focuses on the article. New LAFD chief won't look into who watered down Palisades fire report Deputy Chief Jaime Moore fields questions from city council members before being confirmed as the new LAFD chief after a unanimous vote by the L.A. City Council on Nov. 14. This is read by an automated voice. Please report any issues or inconsistencies here . LAFD Chief Jaime Moore said he is taking a forward-looking approach and not seeking to assign blame for changes to the report.

California's wildfire season keeps getting worse over the years, overwhelming the emergency response teams. These fires cause massive destruction to both property and human life. Because of these reasons, there's a growing need for accurate and practical predictions that can help assist with resources allocation for the Wildfire managers or the response teams. In this research, we built machine learning models to predict the number of days it will require to fully contain a wildfire in California. Here, we addressed an important gap in the current literature. Most prior research has concentrated on wildfire risk or how fires spread, and the few that examine the duration typically predict it in broader categories rather than a continuous measure. This research treats the wildfire duration prediction as a regression task, which allows for more detailed and precise forecasts rather than just the broader categorical predictions used in prior work. We built the models by combining three publicly available datasets from California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP). This study compared the performance of baseline ensemble regressor, Random Forest and XGBoost, with a Long Short-Term Memory (LSTM) neural network. The results show that the XGBoost model slightly outperforms the Random Forest model, likely due to its superior handling of static features in the dataset. The LSTM model, on the other hand, performed worse than the ensemble models because the dataset lacked temporal features. Overall, this study shows that, depending on the feature availability, Wildfire managers or Fire management authorities can select the most appropriate model to accurately predict wildfire containment duration and allocate resources effectively.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Firefighters evacuated residents and their pets from a nine-storey apartment building in Ukraine's Sumy region after a Russian drone strike. The strikes come as Ukrainian President Volodymyr Zelenskyy met with leaders of the UK, France and Germany in London to discuss the US peace plan.

In crowd evacuation the time interval before decisive movement towards a safe place is defined as the pre-evacuation phase, and it has crucial impact on the total time required for safe egress. This process mainly refers to situation awareness and response to an external stressors, e.g., fire alarms. Due to the complexity of human cognitive process, simulation is used to study this important time interval. In this paper a binary decision process is formulated to simulate pre-evacuation time of many evacuees in a given social context. The model combines the classic opinion dynamics (the French-DeGroot model) with binary phase transition to describe how group pre-evacuation time emerges from individual interaction. The model parameters are quantitatively meaningful to human factors research within socio-psychological background, e.g., whether an individual is stubborn or open-minded, or what kind of the social topology exists among the individuals and how it matters in aggregating individuals into social groups. The modeling framework also describes collective motion of many evacuee agents in a planar space, and the resulting multi-agent system is partly similar to the Vicsek flocking model, and it is meaningful to explore complex social behavior during phase transition of a non-equilibrium process.

Abstract--Autonomous robotic systems heavily rely on environment knowledge to safely navigate. For search & rescue, a flying robot requires robust real-time perception, enabled by complementary sensors. IMU data constrains acceleration and rotation, whereas LiDAR measures accurate distances around the robot. Our new scan window uses non-uniform temporal knot placement to ensure continuity over the whole trajectory without additional scan delay. Moreover, we accelerate essential covariance and GMM computations with Kronecker sums and products by a factor of 3.3. An unscented transform de-skews surfels, while a splitting into intra-scan segments facilitates motion compensation during spline optimization. Complementary soft constraints on relative poses and preintegrated IMU pseudo-measurements further improve robustness and accuracy. ELIABLE real-time perception is essential for robotic autonomy. In particular, accurate mapping and ego-motion estimation are key components for safe interaction in complex and unstructured environments. Due to their precision and measurement density, modern LiDARs are often used in these scenarios, e.g., in the DARP A Subterranean Challenge [1], [2]. Sensor motion during scanning distorts the point cloud and degrades the quality of the map. This intra-scan motion is either compensated by de-skewing prior to registration [3], [4], [5], [6] or by modeling it with a continuous-time trajectory [7], [8], [9]. The former uses the previous state estimate and, optionally, an IMU to predict the motion and transform points to a common reference time. However, this comes at the cost of reduced real-time capability and requires either costly reintegration of surfels [9] or a limited number of selected pointwise features [e.g., CT -ICP [7], CLINS [8]]. To overcome these limitations of continuous-time methods, our novel real-time LiDAR-inertial odometry (LIO) jointly optimizes temporally partitioned scan segments (Figure 1) by registering multi-resolution surfel maps while an unscented transform (UT) compensates the intra-surfel motion. Manuscript received October XX, 2025; revised XX, 2025.