Travel chaos warning as hazardous'radiation fog' alert is issued in three states Real reason Bill Hader and Ali Wong's two-year relationship ended: Insiders reveal open secret about him in Hollywood... his cruel nickname... and his month from hell after Reiner murders horror It's madness NOT to annex Greenland: SCOTT JENNINGS spells out, as only he can, why America must act... before its enemies strike Kendall Jenner finally breaks silence on the rumors she's secretly a lesbian Real reason ICE refused to let medics rush to aid of Renee Nicole Good after she was shot dead in her car... as shocking video spread like wildfire The foods that actually block the body from gaining weight... even in people who eat high-fat diets Shocking study linking covid jabs and cancer'censored' by mysterious cyberattack Peppers will help protect you from the'super flu'... but which color you eat matters I gave up a middle-class family life at 40 to become an escort. Years later I discovered a common condition that affects so many women was to blame. Painful cause of death revealed for adorable child, 4, found dead in the woods two miles from dad's home Insiders reveal how the Reiner family decided to ax'despicable' Nick's legal fund: 'He's on his own' No nonsense uncle humiliates rude women for singing and talking during Broadway performance of Mamma Mia! - then has them thrown out of theater The REAL Princess Catherine: On her birthday, an intimate portrait of her marriage, how she finally solved the Meghan problem, her brave cancer fight... and a thrilling new rumor about her in America'Best medical drama ever' rockets up the Netflix charts as'broken' fans left sobbing by'perfect' ending after binge-watching every episode Inside NASA's high-stakes plan to evacuate astronauts from the ISS after medical emergency NASA is preparing to conduct its first-ever medical evacuation from the International Space Station (ISS), activating a contingency plan to return a crew to Earth months ahead of schedule. The plan, developed decades ago for medical emergencies in space, has never before been implemented during an ISS mission, agency officials said Thursday. Under the program, the returning astronauts will seal themselves inside the capsule, undock from the ISS, perform a controlled departure and reenter Earth's atmosphere for a parachute-assisted splashdown in the Pacific Ocean off the California coast.

Traditional data sources on bushfire evacuation behaviour, such as quantitative surveys and manual observations have severe limitations. Mining social media data related to bushfire evacuations promises to close this gap by allowing the collection and processing of a large amount of behavioural data, which are low-cost, accurate, possibly including location information and rich contextual information. However, social media data have many limitations, such as being scattered, incomplete, informal, etc. Together, these limitations represent several challenges to their usefulness to better understand bushfire evacuation. To overcome these challenges and provide guidance on which and how social media data can be used, this scoping review of the literature reports on recent advances in relevant data mining techniques. In addition, future applications and open problems are discussed. We envision future applications such as evacuation model calibration and validation, emergency communication, personalised evacuation training, and resource allocation for evacuation preparedness. We identify open problems such as data quality, bias and representativeness, geolocation accuracy, contextual understanding, crisis-specific lexicon and semantics, and multimodal data interpretation.

Autonomous drone technology holds significant promise for enhancing search and rescue operations during evacuations by guiding humans toward safety and supporting broader emergency response efforts. However, their application in dynamic, real-time evacuation support remains limited. Existing models often overlook the psychological and emotional complexity of human behavior under extreme stress. In real-world fire scenarios, evacuees frequently deviate from designated safe routes due to panic and uncertainty. To address these challenges, this paper presents a multi-agent coordination framework in which autonomous Unmanned Aerial Vehicles (UAVs) assist human evacuees in real-time by locating, intercepting, and guiding them to safety under uncertain conditions. We model the problem as a Partially Observable Markov Decision Process (POMDP), where two heterogeneous UAV agents, a high-level rescuer (HLR) and a low-level rescuer (LLR), coordinate through shared observations and complementary capabilities. Human behavior is captured using an agent-based model grounded in empirical psychology, where panic dynamically affects decision-making and movement in response to environmental stimuli. The environment features stochastic fire spread, unknown evacuee locations, and limited visibility, requiring UAVs to plan over long horizons to search for humans and adapt in real-time. Our framework employs the Proximal Policy Optimization (PPO) algorithm with recurrent policies to enable robust decision-making in partially observable settings. Simulation results demonstrate that the UAV team can rapidly locate and intercept evacuees, significantly reducing the time required for them to reach safety compared to scenarios without UAV assistance.

How much of Europe's oil still comes from Russia? A Russian drone has killed two Ukrainian journalists and wounded another in the eastern Ukrainian city of Kramatorsk, according to their outlet and the regional governor of the Donetsk region. Freedom Media, a state-funded news organisation, said on Thursday that Olena Gramova, 43, and Yevgen Karmazin, 33, had been killed by a Russian Lancet drone while in their car at a petrol station in the industrial city. Another reporter, Alexander Kolychev, was hospitalised after the attack. Freedom Media said that Gramova, a native of Yenakiieve in the Donetsk region, had originally trained as a "finance specialist", but turned to journalism in 2014, the year when Russia annexed Ukraine's Crimean peninsula, and started arming a separatist movement in Donetsk and Luhansk in the Donbas.

A total of more than 3400 public shootings have occurred in the United States between 2016 and 2022. Among these, 25.1% of them took place in an educational institution, 29.4% at the workplace including office buildings, 19.6% in retail store locations, and 13.4% in restaurants and bars. During these critical scenarios, making the right decisions while evacuating can make the difference between life and death. However, emergency evacuation is intensely stressful, which along with the lack of verifiable real-time information may lead to fatal incorrect decisions. To tackle this problem, we developed a multi-route routing optimization algorithm that determines multiple optimal safe routes for each evacuee while accounting for available capacity along the route, thus reducing the threat of crowding and bottlenecking. Overall, our algorithm reduces the total casualties by 34.16% and 53.3%, compared to our previous routing algorithm without capacity constraints and an expert-advised routing strategy respectively. Further, our approach to reduce crowding resulted in an approximate 50% reduction in occupancy in key bottlenecking nodes compared to both of the other evacuation algorithms.

A growing leak on the International Space Station has sparked fears that astronauts on board may need to evacuate, including the two stranded by Boeing's Starliner. All seven astronauts have been forced into the US side of the orbiting laboratory due to 50 'areas of concern' and four cracks in a Russian-made module. If the leaks become severe, the space station could rapidly lose oxygen and pressure. The moment Houston sounds the alarm of a threat, astronauts would have to race to shut the hatch of the leaking section and head to'lifeboats' docked on the ship. A spaceflight expert told DailyMail.com

We present a viewpoint-based non-linear Model Predictive Control (MPC) for evacuation guiding robots. Specifically, the proposed MPC algorithm enables evacuation guiding robots to track and guide cooperative human targets in emergency scenarios. Our algorithm accounts for the environment layout as well as distances between the robot and human target and distance to the goal location. A key challenge for evacuation guiding robot is the trade-off between its planned motion for leading the target toward a goal position and staying in the target's viewpoint while maintaining line-of-sight for guiding. We illustrate the effectiveness of our proposed evacuation guiding algorithm in both simulated and real-world environments with an Unmanned Aerial Vehicle (UAV) guiding a human. Our results suggest that using the contextual information from the environment for motion planning, increases the visibility of the guiding UAV to the human while achieving faster total evacuation time.

Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios. We first, aggregate operational data from multiple airports and then determine the optimal count of evacuation flights to maximize the impacted airport's outgoing capacity without impeding regular air traffic. We then propose a novel Neural Network (NN) accelerated Genetic Algorithm(GA) for evacuation planning. Our experiments show that integration yielded comparable results but with smaller computational overhead. We find that the utilization of a NN enhances the efficiency of a GA, facilitating more rapid convergence even when operating with a reduced population size. This effectiveness persists even when the model is trained on data from airports different from those under test.

Accurate traffic prediction is vital for effective traffic management during hurricane evacuation. This paper proposes a predictive modeling system that integrates Multilayer Perceptron (MLP) and Long-Short Term Memory (LSTM) models to capture both long-term congestion patterns and short-term speed patterns. Leveraging various input variables, including archived traffic data, spatial-temporal road network information, and hurricane forecast data, the framework is designed to address challenges posed by heterogeneous human behaviors, limited evacuation data, and hurricane event uncertainties. Deployed in a real-world traffic prediction system in Louisiana, the model achieved an 82% accuracy in predicting long-term congestion states over a 6-hour period during a 7-day hurricane-impacted duration. The short-term speed prediction model exhibited Mean Absolute Percentage Errors (MAPEs) ranging from 7% to 13% across evacuation horizons from 1 to 6 hours. Evaluation results underscore the model's potential to enhance traffic management during hurricane evacuations, and real-world deployment highlights its adaptability and scalability in diverse hurricane scenarios within extensive transportation networks.

A smart city solution toward future 6G network deployment allows small and medium sized enterprises (SMEs), industry, and government entities to connect with the infrastructures and play a crucial role in enhancing emergency preparedness with advanced sensors. The objective of this work is to propose a set of coordinated technological solutions to transform an existing emergency response system into an intelligent interactive system, thereby improving the public services and the quality of life for residents at home, on road, in hospitals, transport hubs, etc. In this context, we consider a city wide view from three different application scenes that are closely related to peoples daily life, to optimize the actions taken at relevant departments. Therefore, using artificial intelligence (AI) and machine learning (ML) techniques to enable the next generation connected vehicle experiences, we specifically focus on accidents happening in indoor households, urban roads, and at large public facilities. This smart interactive response system will benefit from advanced sensor fusion and AI by formulating a real time dynamic model.