Three aid workers killed, 4 wounded in RSF drone attack in Sudan's Kordofan At least three aid workers have been killed and four others wounded in a drone attack by the paramilitary Rapid Support Forces (RSF) on an aid convoy in Sudan's South Kordofan state, according to the Sudan Doctors Network, in the latest carnage against civilians caught up in the nation's brutal civil war. The convoy of trucks carrying food and humanitarian supplies was targeted by the RSF, and its ally, the Sudan People's Liberation Movement-North, while travelling through the Kartala area on its way to the cities of Kadugli and Dilling on Thursday. The network said that this attack marked the "second such incident in less than a month, following the shelling of a United Nations aid convoy in the town of Al-Rahad," adding: "this dangerous escalation threatens the safety of humanitarian operations and further exacerbates civilian suffering". The Sudan Doctors Network reiterated its call to the "international community, the United Nations, and human rights organisations to exert urgent and effective pressure on the leadership of the Rapid Support Forces to ensure the protection of aid convoys and their workers, to open safe and sustainable humanitarian corridors, and to hold those responsible for targeting aid accountable". Al Jazeera could not independently verify the latest RSF attack, which came a month after the government-aligned Sudanese Armed Forces (SAF) announced that it had broken a nearly two-year-long RSF siege on Dilling.

We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate decentralized collective violence in spatially distributed systems. Building on Epstein's Agent\_Zero framework, we extend the original model with eight behavioral enhancements: age-based impulse control; memory-based risk estimation; affect-cognition coupling; endogenous destructive radius; fight-or-flight dynamics; affective homophily; retaliatory damage; and multi-agent coordination. These additions allow agents to adapt based on internal states, previous experiences, and social feedback, producing emergent dynamics such as protest asymmetries, escalation cycles, and localized retaliation. Implemented in Python using the Mesa ABM framework, AgentZero++ enables modular experimentation and visualization of how micro-level cognitive heterogeneity shapes macro-level conflict patterns. Our results highlight how small variations in memory, reactivity, and affective alignment can amplify or dampen unrest through feedback loops. By explicitly modeling emotional thresholds, identity-driven behavior, and adaptive networks, this work contributes a flexible and extensible platform for analyzing affective contagion and psychologically grounded collective action.

NVIDIA CEO and co-founder Jensen Huang commends President Donald Trump's A.I. agenda and outlines what the country's job future will look like on'Special Report.' Classical and character-based education may seem to some antiquated concepts in the new AI-driven world. However, two recent and prominent AI developments definitively prove the opposite to be true. Going back to our nation's founding, great minds were universal in the belief that the survival of the Republic depended on an educated and virtuous public. Now, if AI experts are to be believed, classical and character education is fundamental to the very survival of humanity.

When you think of Nintendo, it's almost impossible not to picture Donkey Kong. Yet despite Donkers' undeniable place in gaming history – and obligatory appearances in Smash Bros and Mario Kart – for the last few console generations, Donkey Kong platformers have been MIA. Enter DK's first standalone adventure in 11 years, Donkey Kong Bananza. While Mario's recent adventures saw him exploring the reaches of outer space or deftly possessing enemies with an anthropomorphic hat, DK's grand return is all about primal rage. As you smash and punch your way through walls, floors and ceilings, you can burrow all the way to the ground below, forging new paths and unearthing hidden treasures.

A fragile ceasefire remains in place between Israel and Iran, one day after US President Donald Trump announced a truce, ending 12 days of fighting that erupted following Israeli strikes on Tehran's nuclear and military sites. An analysis of data from the Armed Conflict Location and Event Data Project (ACLED) shows that between October 7, 2023, and just before Israel attacked Iran on June 13, 2025, Israel carried out nearly 35,000 recorded attacks across five countries: the occupied Palestinian territory, Lebanon, Syria, Yemen, and Iran. These attacks include air and drone strikes, shelling and missile attacks, remote explosives, and property destruction. The majority of attacks have been on Palestinian territory with at least 18,235 recorded incidents, followed by Lebanon (15,520), Syria (616), Iran (58) and Yemen (39). While the bulk of Israel's attacks have concentrated on nearby Gaza, the occupied West Bank, and Lebanon, its military operations have also reached far beyond its immediate borders.

The most accurate clock in space launches within days and will begin building a highly synchronised network out of the best clocks on Earth. But the project, decades in preparation, will only operate for a few years before it burns up as the International Space Station deorbits at the end of the decade. NASA's most accurate atomic clock will be tested on a mission to Venus The Atomic Clock Ensemble in Space (ACES) is a European Space Agency (ESA) mission that will generate a time signal with unprecedented accuracy and then transmit it via laser to nine ground stations as it passes overhead at 27,000 kilometres per hour. This network of clocks will be in extremely close synchronisation and provide highly accurate timekeeping around the world. The result is that ACES will be able to test Einstein's theory of general relativity, which says that the passing of time is affected by the strength of gravity, with great accuracy.

Recent advancements in Neural Combinatorial Optimization (NCO) have shown promise in solving routing problems like the Traveling Salesman Problem (TSP) and Capacitated Vehicle Routing Problem (CVRP) without handcrafted designs. Research in this domain has explored two primary categories of methods: iterative and non-iterative. While non-iterative methods struggle to generate near-optimal solutions directly, iterative methods simplify the task by learning local search steps. However, existing iterative methods are often limited by restricted neighborhood searches, leading to suboptimal results. To address this limitation, we propose a novel approach that extends the search to larger neighborhoods by learning a destroy-and-repair strategy. Specifically, we introduce a Destroy-and-Repair framework based on Hyper-Graphs (DRHG). This framework reduces consecutive intact edges to hyper-edges, allowing the model to pay more attention to the destroyed part and decrease the complexity of encoding all nodes. Experiments demonstrate that DRHG achieves stateof-the-art performance on TSP with up to 10,000 nodes and shows strong generalization to real-world TSPLib and CVRPLib problems.

This month, humanity will learn just how close we are to annihilation. Every January, the Bulletin of the Atomic Scientists (BAS) sets a new time for the Doomsday Clock - the symbolic scale for humanity's proximity to the apocalypse. Last year, scientists left the clock sitting at 90 seconds to midnight - the closest humanity had come to destruction since the creation of the atomic bomb. But with war still raging in Ukraine and chaos across the Middle East, experts say that the risk of nuclear war is now'far too high'. Dr Haydn Belfield, research associate at the Centre for the Study of Existential Risk, told MailOnline: 'We are probably closer to nuclear war than at any point in the last forty years.'

In this paper, we propose TextDestroyer, the first training- and annotation-free method for scene text destruction using a pre-trained diffusion model. Existing scene text removal models require complex annotation and retraining, and may leave faint yet recognizable text information, compromising privacy protection and content concealment. TextDestroyer addresses these issues by employing a three-stage hierarchical process to obtain accurate text masks. Our method scrambles text areas in the latent start code using a Gaussian distribution before reconstruction. During the diffusion denoising process, self-attention key and value are referenced from the original latent to restore the compromised background. Latent codes saved at each inversion step are used for replacement during reconstruction, ensuring perfect background restoration. The advantages of TextDestroyer include: (1) it eliminates labor-intensive data annotation and resource-intensive training; (2) it achieves more thorough text destruction, preventing recognizable traces; and (3) it demonstrates better generalization capabilities, performing well on both real-world scenes and generated images.

Teams of mobile [aerial, ground, or aquatic] robots have applications in resource delivery, patrolling, information-gathering, agriculture, forest fire fighting, chemical plume source localization and mapping, and search-and-rescue. Robot teams traversing hazardous environments -- with e.g. rough terrain or seas, strong winds, or adversaries capable of attacking or capturing robots -- should plan and coordinate their trails in consideration of risks of disablement, destruction, or capture. Specifically, the robots should take the safest trails, coordinate their trails to cooperatively achieve the team-level objective with robustness to robot failures, and balance the reward from visiting locations against risks of robot losses. Herein, we consider bi-objective trail-planning for a mobile team of robots orienteering in a hazardous environment. The hazardous environment is abstracted as a directed graph whose arcs, when traversed by a robot, present known probabilities of survival. Each node of the graph offers a reward to the team if visited by a robot (which e.g. delivers a good to or images the node). We wish to search for the Pareto-optimal robot-team trail plans that maximize two [conflicting] team objectives: the expected (i) team reward and (ii) number of robots that survive the mission. A human decision-maker can then select trail plans that balance, according to their values, reward and robot survival. We implement ant colony optimization, guided by heuristics, to search for the Pareto-optimal set of robot team trail plans. As a case study, we illustrate with an information-gathering mission in an art museum.