Reactor no 3 lost off-site power for about 24 hours after the attack. Reactor no 3 lost off-site power for about 24 hours after the attack. A drone strike that cut off external power to a nuclear reactor in the United Arab Emirates this week has revived concerns over the safety of nuclear plants during wartime. Reactor no 3 at the Barakah nuclear plant lost vital off-site power for about 24 hours after the attack on Sunday, forcing it to rely on emergency diesel generators. The UAE's defence ministry said on Tuesday that three drones targeting the plant had originated from Iraqi territory, suggesting a pro-Iranian proxy group was most likely to have been behind the strike.

Will Gulf states join war? What is the UAE's Barakah nuclear plant, nearly hit by a drone? A drone attack that caused a fire close to the Barakah Nuclear Energy Plant in the United Arab Emirates has raised further concerns about nuclear security and military escalation in the Gulf as discussions of peace between Iran and the United States hang in the balance. Barakah was the first nuclear power station to be built on the Arabian Peninsula. What is the Barakah Nuclear Energy Plant? Barakah is a nuclear energy plant located in Al Dhafra, the largest municipal region of the emirate of Abu Dhabi.

The United Arab Emirates is investigating the source of a drone strike which triggered a fire near a nuclear power station, officials have said. The country's defence ministry said three drones had entered the UAE from the western border direction on Sunday. While two were intercepted, the third drone struck an electrical generator outside the inner perimeter of the Barakah Nuclear Power Plant in Abu Dhabi. No injuries were reported and there was no impact on radiological safety levels, local authorities said. The country's defence ministry said in a statement that investigations were under way to determine the source of the attacks.

Could the war trigger a hunger crisis? How well do you know Iran? Drone strike sparks fire on perimeter of UAE's Barakah nuclear power plant A drone strike has sparked a fire on the perimeter of the Barakah Nuclear Energy Plant in the United Arab Emirates (UAE), raising new concerns over a potential new regional escalation amid a fragile ceasefire between Iran and the United States. Authorities in Abu Dhabi said the blaze broke out at an electrical generator outside the plant's inner perimeter in the Al Dhafra region on Sunday. No injuries were reported, and officials said radiation levels remained normal.

How well do you know Iran? The United Arab Emirates has said its air defences intercepted ballistic and cruise missiles fired from Iran, while a fire was reported at an oil facility in Fujairah after a suspected drone attack. Tehran has not officially commented. Qatar, Jordan, Saudi Arabia and Kuwait, along with the Gulf Cooperation Council (GCC) and the European Union, have condemned the suspected Iranian strike on the UAE. The incident comes as tensions rise, with United States President Donald Trump warning Iran would be "blown off the face of the earth" if US Navy ships are targeted in the Strait of Hormuz.

Is Dubai's glossy image under threat? Stephanie Baker had been celebrating her birthday with friends at a bar on Palm Jumeirah - Dubai's iconic man-made palm-shaped island lined with luxury hotels and beach clubs. But as the group stepped outside to head to another nearby venue, something unusual streaked across the night sky. Moments later, debris from a drone struck the five-star Fairmont hotel - Baker and her friends were standing right across the street. We all were scared, she says.

Comparative evidence of how two Gulf Cooperation Council (GCC) states translate artificial intelligence (AI) ambitions into post-New Public Management (post-NPM) outcomes are scarce because most studies focus on Western democracies. To fill this gap, we examine constitutional, collective choice, and operational rules that shape AI uptake in two contrasting GCC members, the United Arab Emirates (UAE) and Kuwait, and whether they foster citizen centricity, collaborative governance, and public value creation. Anchored in Ostrom's Institutional Analysis and Development framework, the study integrates a most similar/ most different systems design with multiple sources: 62 public documents issued between 2018 and 2025, embedded UAE cases (Smart Dubai and MBZUAI), and 39 interviews with officials conducted from Aug 2024 to May 2025. Dual coding and process tracing connect rule configurations to AI performance. Our cross-case analysis identifies four mutually reinforcing mechanisms behind divergent trajectories. In the UAE, concentrated authority, credible sanctions, pro-innovation narratives, and flexible reinvestment rules transform pilots into hundreds of operating services and significant recycled savings. Kuwait's dispersed veto points, exhortative sanctions, cautious discourse, and lapsed AI budgets, by contrast, confine initiatives to pilot mode de - spite equivalent fiscal resources. These findings refine institutional theory by showing that vertical rule coherence, not wealth, determines AI's public value yield, and temper post-NPM optimism by revealing that efficiency metrics advance societal goals only when backed by enforceable safeguards. To curb ethics washing and test the transferability of these mechanisms beyond the GCC, future research should track rule diffusion over time, experiment with blended legitimacy-efficiency scorecards, and investigate how narrative framing shapes citizen consent for data sharing.

The rapid expansion of artificial intelligence (AI) in the Gulf Cooperation Council (GCC) raises a central question: are investments in compute infrastructure matched by an equally robust build-out of skills, incentives, and governance? Grounded in socio-technical systems (STS) theory, this mixed-methods study audits workforce preparedness across Kingdom of Saudi Arabia (KSA), the United Arab Emirates (UAE), Qatar, Kuwait, Bahrain, and Oman. We combine term frequency--inverse document frequency (TF--IDF) analysis of six national AI strategies (NASs), an inventory of 47 publicly disclosed AI initiatives (January 2017--April 2025), paired case studies, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) and the Saudi Data & Artificial Intelligence Authority (SDAIA) Academy, and a scenario matrix linking oil-revenue slack (technical capacity) to regulatory coherence (social alignment). Across the corpus, 34/47 initiatives (0.72; 95% Wilson CI 0.58--0.83) exhibit joint social--technical design; country-level indices span 0.57--0.90 (small n; intervals overlap). Scenario results suggest that, under our modeled conditions, regulatory convergence plausibly binds outcomes more than fiscal capacity: fragmented rules can offset high oil revenues, while harmonized standards help preserve progress under austerity. We also identify an emerging two-track talent system, research elites versus rapidly trained practitioners, that risks labor-market bifurcation without bridging mechanisms. By extending STS inquiry to oil-rich, state-led economies, the study refines theory and sets a research agenda focused on longitudinal coupling metrics, ethnographies of coordination, and outcome-based performance indicators.

K2 Think compares well with reasoning models from OpenAI and DeepSeek but is smaller and more efficient, say researchers based in Abu Dhabi. The United Arab Emirates (UAE) has released an open source model that performs advanced reasoning as well as the best offerings from both the United States and China--one of the strongest signs so far that the nation's big investments in artificial intelligence are starting to pay off. The new model, K2 Think, comes from researchers at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) located in UAE's capital Abu Dhabi. The model--one of the first so-called "sovereign" AI models that incorporates technical advances needed for reasoning--is being made available for free by G42, an Emirati tech conglomerate backed by Abu Dhabi's sovereign wealth funds. G42 is running the model on a cluster of Cerberas chips, an alternative to Nvidia's hardware.

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.