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.

A South Korean court has handed a 15-year prison sentence to the boss of a lithium battery maker after a deadly fire last year. In June 2024, a blaze at a plant in Hwaseong city, about 45km (28 miles) south of the capital Seoul, killed 23 people, including 18 foreign workers, and injured eight others. The court found the blaze was an anticipated disaster and that Aricell chief executive Park Soon-kwan and other executives had caused the deaths of the workers. It is the longest jail term imposed under the country's industrial safety law, which punishes owners or bosses of firms with at least a year in prison, or fines of up to 1 billion won ($717,000; £530,000), for fatal incidents. Prosecutors had sought a 20-year term, arguing that company executives had made changes to the plant that meant it was difficult for workers to escape the fire.

Figure 1: This paper introduces BLAZE, a Phase 1 - Phase 2 Affine Geometric Heat Flow (AGHF) framework, to rapidly solve optimal control problems while respecting robot constraints and avoiding obstacles. It begins with an initial trajectory (shown in orange with the color gradient illustrating the evolution in time starting from darkest and going to lightest) that may violate constraints (e.g., the second and fourth pose of the arm are in collision with the boxes and outlined in red). If the initial trajectory is infeasible, BLAZE enters Phase 1, where it evolves the trajectory into a trajectory that satisfies all constraints (e.g., in the blue trajectory, the Kinova arm has been moved out of collision with the boxes). Once the trajectory satisfies all constraints, Phase 2 begins, optimizing the motion to minimize a user-specified cost function while maintaining feasibility (optimized trajectory shown green). BLAZE optimizes the trajectory to reach a target configuration while avoiding the obstacles while considering the full dynamical model of the arm. Note that optimal control (including Phase 1 and Phase 2) for this 14 dimensional state space model is completed within 3s while satisfying input, state, and collision avoidance constraints. Abstract --The generation of optimal trajectories for high-dimensional robotic systems under constraints remains computationally challenging due to the need to simultaneously satisfy dynamic feasibility, input limits, and task-specific objectives while searching over high-dimensional spaces. Recent approaches using the Affine Geometric Heat Flow (AGHF) Partial Differential Equation (PDE) have demonstrated promising results, generating dynamically feasible trajectories for complex systems like the Digit V3 humanoid within seconds. These methods efficiently solve trajectory optimization problems over a two-dimensional domain by evolving an initial trajectory to minimize control effort. However, these AGHF approaches are limited to a single type of optimal control problem (i.e., minimizing the integral of squared control norms) and typically require initial guesses that satisfy constraints to ensure satisfactory convergence. These limitations restrict the potential utility of the AGHF PDE especially when trying to synthesize trajectories for robotic systems. This paper generalizes the AGHF formulation to accommodate arbitrary cost functions, significantly expanding the classes of trajectories that can be generated. This work also introduces a Phase 1 - Phase 2 Algorithm that enables the use of constraint-violating initial guesses while guaranteeing satisfactory convergence. The effectiveness of the proposed method is demonstrated through comparative evaluations against state-of-the-art techniques across various dynamical systems and challenging trajectory generation problems. Optimal Control is a powerful tool for motion planning and control of advanced robotic systems.

Huge flames can be seen engulfing a closely-built shanty community in the port area of Manila, in drone footage released by the city's disaster management office. Hundreds of residents have been left without homes, after around 1,000 houses were destroyed in the blaze on Sunday, Manila Fire District said. Dozens of fire engines and fire boats were deployed to tackle the blaze, and the Philippine Air Force sent two helicopters which scooped up up water from Manila Bay to help extinguish the fire. Emergency services have reported no casualties so far, and are yet to identify the cause. The incident comes months after 11 people died in residential fire in the Chinatown district of the Philippine capital.

Software bugs require developers to exert significant effort to identify and resolve them, often consuming about one-third of their time. Bug localization, the process of pinpointing the exact source code files that need modification, is crucial in reducing this effort. Existing bug localization tools, typically reliant on deep learning techniques, face limitations in cross-project applicability and effectiveness in multi-language environments. Recent advancements with Large Language Models (LLMs) offer detailed representations for bug localization. However, they encounter challenges with limited context windows and mapping accuracy. To address these issues, we propose BLAZE, an approach that employs dynamic chunking and hard example learning. First, BLAZE dynamically segments source code to minimize continuity loss. Then, BLAZE fine-tunes a GPT-based model using challenging bug cases, in order to enhance cross-project and cross-language bug localization. To support the capability of BLAZE, we create the BEETLEBOX dataset, which comprises 26,321 bugs from 29 large and thriving open-source projects across five different programming languages (Java, C++, Python, Go, and JavaScript). Our evaluations of BLAZE on three benchmark datasets BEETLEBOX, SWE-Bench, and Ye et al. demonstrate substantial improvements compared to six state-of-the-art baselines. Specifically, BLAZE achieves up to an increase of 120% in Top 1 accuracy, 144% in Mean Average Precision (MAP), and 100% in Mean Reciprocal Rank (MRR). An extensive ablation study confirms the contributions of our pipeline components to the overall performance enhancement.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Firefighters worked for a second day Wednesday to fully extinguish a blaze at a U.S. company's drone plant in Latvia. Local police said nothing had been found so far to indicate sabotage. Latvia's State Fire and Rescue Service was alerted Tuesday afternoon that a fire had broken out at Edge Autonomy's drone production plant in Marupe, a town that borders the capital, Riga. The Baltic News Service said that although the blaze was largely contained by 7 p.m. on Tuesday, firefighters continued work to fully extinguish the fire early Wednesday.\

SpaceX and Tesla CEO Elon Musk posted an outlandish tweet on Monday in which he references the novel The Guns of August, a 500-page book about the early stages of World War I. Musk, 50, captioned the tweet with the name of the book, written by Barbara Tuchman in 1962, along with the entire first paragraph of the book. Barbara Tuchman's 1962 novel was centered on the first month of the Great War and the opening events of WWI, along with the decisions that led to it. Tuchman's book was an immediate bestseller and earned her a Pulitzer Prize for general nonfiction'The muffled tongue of Big Ben tolled nine by the clock as the cortege left the palace, but on history's clock it was sunset, and the sun of the old world was setting in a dying blaze of splendor never to be seen again,' so ends the paragraph. Tuchman's book, centered on the first month of the Great War, was an immediate bestseller and earned her a Pulitzer Prize for general nonfiction. President John F. Kennedy was so impressed with it that he gave a copy to each member of his cabinet and some of his top military advisors and told them to read it.

LOS ANGELES, July 22 (Thomson Reuters Foundation) - Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data and forecasting to the world of firefighting.

Last summer, as Will Harling captained a fire engine trying to control a wildfire that had burst out of northern California's Klamath National Forest, overrun a firebreak, and raced towards his hometown, he got a frustrating email. It was a statistical analysis from Oregon State University forestry researcher Chris Dunn, predicting that the spot where firefighters had built the firebreak, on top of a ridge a few miles out of town, had only a 10% chance of stopping the blaze. "They had spent so many resources building that useless break," said Mr. Harling, who directs the Mid Klamath Watershed Council, and works as a wildland firefighter for the local Karuk Tribe. "The index showed it had no chance," he told the Thomson Reuters Foundation in a phone interview. The Suppression Difficulty Index (SDI) is one of a number of analytical tools Mr. Dunn and other firefighting technology experts are building to bring the latest in machine learning, big data, and forecasting to the world of firefighting.

Editor's Note: At Google Research, we're interested in exploring how technology can help improve people's daily lives and experiences. So it's been an incredible opportunity to work with Thomas Panek, avid runner and President & CEO of Guiding Eyes for the Blind, to apply computer vision for something important in his everyday life: independent exercise. Project Guideline is an early-stage research project that leverages on-device machine learning to allow Thomas to use a phone, headphones and a guideline painted on the ground to run independently. Below, Thomas shares why he collaborated with us on this research project, and what the journey has been like for him. I've always loved to run.