An Immigration and Customs Enforcement (ICE) officer has shot a man in the leg in the US city of Minneapolis, where an ICE agent shot dead a woman last week. In a statement, the Department of Homeland Security (DHS) said federal officers initially pursued the man in a car chase because he was illegally in the US from Venezuela. The City of Minneapolis confirmed a man was shot and taken to hospital for non-life threatening injuries. An ICE officer was also taken to hospital to be treated for injuries, the DHS said. Minneapolis city officials said on X: We understand there is anger.

Police say at least 25 kids used e-bikes and scooters to evade arrest and intimidate drivers. Breakthroughs, discoveries, and DIY tips sent six days a week. Australian police are cracking down on groups of unruly teenagers who they say are using deceptively speedy e-bikes and scooters to engage in "antisocial riding behavior." Their solution: confiscate the popular micromobility devices and crush them. The roundup, dubbed Operation Moorhead, began last week in the suburbs of Perth in southwestern Australia.

Abstract-- This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (UA Vs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The UA V identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-robot systems can augment human capabilities in disaster response scenarios to maximize lives saved. I. INTRODUCTION Robotics has long sought to augment human capabilities in hazardous scenarios. Mass-casualty incidents (MCIs), such as those resulting from natural disasters, bombings, plane crashes, or industrial chemical spills, present an opportunity for robotic systems to assist first responders. The critical first step of providing medical assistance during MCIs is primary triage: the initial process of locating victims at the site of the MCI and assessing the severity of their injuries to prioritize treatment, which is essential to optimizing survival outcomes. Traditionally, primary triage relies on human responders who may face significant risk and information overload [1], underscoring the potential for automated systems to mitigate these challenges. While prior efforts have explored the use of air-ground robotic teams for search and exploration in disaster zones [2]-[5], few systems have focused specifically on rapid triage. Existing approaches typically solve parts of the problem in isolation without integrating comprehensive triage functions. For example, air-ground teams have also been developed to find and localize objects of interest [3], [6] Authors are with the GRASP Lab, School of Engineering and Applied Sciences, University of Pennsylvania. Authors are with the Perelman School of Medicine, University of Pennsylvania. This work was supported by the DARP A Triage Challenge under grant #HR001123S0011.

Embedded Sensing Group ESG Institute of Computer Science in V orarlberg ICV, University of St. Gallen HSG, Switzerland E-mail: leonardo.rossi@student.unisg.ch, Abstract--Triathlon training, which involves high-volume swimming, cycling, and running, places athletes at substantial risk for overuse injuries due to repetitive physiological stress. Current injury prediction approaches primarily rely on training load metrics, often neglecting critical factors such as sleep quality, stress, and individual lifestyle patterns that significantly influence recovery and injury susceptibility. We introduce a novel synthetic data generation framework tailored explicitly for triathlon. This framework generates physiologically plausible athlete profiles, simulates individualized training programs that incorporate periodization and load-management principles, and integrates daily-life factors such as sleep quality, stress levels, and recovery states. We evaluated machine learning models (LASSO, Random Forest, and XGBoost) showing high predictive performance (AUC up to 0.86), identifying sleep disturbances, heart rate variability, and stress as critical early indicators of injury risk. This wearable-driven approach not only enhances injury prediction accuracy but also provides a practical solution to overcoming real-world data limitations, offering a pathway toward a holistic, context-aware athlete monitoring. Triathlon is a demanding multi-sport discipline that combines swimming, cycling, and running.

Avoid injury and improve efficiency with tips from a physical therapist. Don't be a snow hero. Breakthroughs, discoveries, and DIY tips sent every weekday. You know, for life's most essential resource, water knows a hundred ways to kill you if you're not careful. When it's not trying to drown you in its pools and coastlines during the summer, it shape-shifts to snow in the winter, piling up emergency room visits for those forced to shovel it.

A surrealist painting from the 1940s by Frida Kahlo has sold for $54.7m (£41.8m) - shattering the auction record for an artwork by a female artist. The painting went for more than 1,000 times its original auction price in 1980, after a tense bidding battle between two collectors, according to the Sotheby's auction house. The auction also broke the previous record for the highest amount paid for a Kahlo portrait, which sold for $34.9 million in 2021. The work - titled El sueño (la cama), which is translated to The dream (The bed) - depicts Kahlo asleep in a canopy bed beneath a skeleton entwined with dynamite. It marks one of the Mexican artist's most psychologically charged self portraits, Sotheby's said, and was painted during a turbulent chapter in Kahlo's life - the year her former lover was assassinated and shortly after her divorce and remarriage.

Small Language Models (SLMs) have potential to be used for automatically labelling and identifying aspects of text data for medicine/health-related purposes from documents and the web. As their resource requirements are significantly lower than Large Language Models (LLMs), these can be deployed potentially on more types of devices. SLMs often are benchmarked on health/medicine-related tasks, such as MedQA, although performance on these can vary especially depending on the size of the model in terms of number of parameters. Furthermore, these test results may not necessarily reflect real-world performance regarding the automatic labelling or identification of texts in documents and the web. As a result, we compared topic-relatedness scores from Microsofts phi-3-mini-4k-instruct SLM to the topic-relatedness scores from 7 human evaluators on 1144 samples of medical/health-related texts and 1117 samples of sports injury-related texts. These texts were from a larger dataset of about 9 million news headlines, each of which were processed and assigned scores by phi-3-mini-4k-instruct. Our sample was selected (filtered) based on 1 (low filtering) or more (high filtering) Boolean conditions on the phi-3 SLM scores. We found low-moderate significant correlations between the scores from the SLM and human evaluators for sports injury texts with low filtering (\r{ho} = 0.3413, p < 0.001) and medicine/health texts with high filtering (\r{ho} = 0.3854, p < 0.001), and low significant correlation for medicine/health texts with low filtering (\r{ho} = 0.2255, p < 0.001). There was negligible, insignificant correlation for sports injury-related texts with high filtering (\r{ho} = 0.0318, p = 0.4466).

Training vision models to detect workplace hazards accurately requires realistic images of unsafe conditions that could lead to accidents. However, acquiring such datasets is difficult because capturing accident-triggering scenarios as they occur is nearly impossible. To overcome this limitation, this study presents a novel scene graph-guided generative AI framework that synthesizes photorealistic images of hazardous scenarios grounded in historical Occupational Safety and Health Administration (OSHA) accident reports. OSHA narratives are analyzed using GPT-4o to extract structured hazard reasoning, which is converted into object-level scene graphs capturing spatial and contextual relationships essential for understanding risk. These graphs guide a text-to-image diffusion model to generate compositionally accurate hazard scenes. To evaluate the realism and semantic fidelity of the generated data, a visual question answering (VQA) framework is introduced. Across four state-of-the-art generative models, the proposed VQA Graph Score outperforms CLIP and BLIP metrics based on entropy-based validation, confirming its higher discriminative sensitivity.

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? At least 10 people have been killed, and more parts of Ukraine have been plunged into darkness, after another night of intense Russian attacks across the country, local authorities said, as diplomatic momentum to end the nearly four-year war falters. Ukraine's military announced on Saturday morning that hundreds of Russian drones, as well as missiles launched from the air, ground and sea, targeted critical infrastructure, a frequent Kremlin target as another harsh winter of war looms. Most of the missiles went through defences, with only nine successfully shot down, but 406 of the drones were intercepted.

Science Archaeology Medieval duke's remains recount his grisly murder In 1272, Hungary's Béla of Macsó received over 23 sword gashes-and more. Breakthroughs, discoveries, and DIY tips sent every weekday. In 1272 CE, a Hungarian duke was murdered in cold blood. Details surrounding the grisly killing of the 13th century Hungarian duke named Béla of Macsó have remained murky for centuries. The duke met his demise at the hand of enemies, but far less is known about what motivated his killers or how the attack really unfolded.