This paper addresses the growing concern of cascading extreme events, such as an extreme earthquake followed by a tsunami, by presenting a novel method for risk assessment focused on these domino effects. The proposed approach develops an extreme value theory framework within a Kolmogorov-Arnold network (KAN) to estimate the probability of one extreme event triggering another, conditionally on a feature vector. An extra layer is added to the KAN's architecture to enforce the definition of the parameter of interest within the unit interval, and we refer to the resulting neural model as KANE (KAN with Natural Enforcement). The proposed method is backed by exhaustive numerical studies and further illustrated with real-world applications to seismology and climatology.

It will take six months to a year to analyze a tiny sample of radioactive debris retrieved by a robot from Tokyo Electric Power Company Holdings' crippled Fukushima No. 1 nuclear plant, its operator said Thursday. The analysis could shed light on radioactivity levels and the chemical structure of the fuel debris -- a key part of preparation for the decadeslong decommissioning process. Around 880 tons of hazardous material remain at the Fukushima plant, more than 13 years after a tsunami caused by an earthquake triggered one of the world's worst nuclear incidents. Last week, the sample, weighing just below 0.7 gram -- equivalent to about one raisin -- was delivered to a research lab near Tokyo for analysis. It had been removed from a reactor by an extendible robotic device in a tricky operation suspended several times by technical problems.

Eerie new drone footage has for the first time revealed the extent of the damage to the Fukushima nuclear power plant 13 years on from its meltdown. The plant's operators, Tokyo Electric Power Company Holdings, or TEPCO, released 12 photos from inside the site, which are the first ever images from inside the main structural support called the pedestal in the hardest-hit reactor's primary containment vessel, an area directly under the reactor's core. Officials had long hoped to reach the area to examine the core and melted nuclear fuel which dripped there when the plant's cooling systems were damaged by a massive earthquake and tsunami in 2011. The high-definition color images captured by the drones show brown objects with various shapes and sizes dangling from various locations in the pedestal. Parts of the control-rod drive mechanism, which controls the nuclear chain reaction, and other equipment attached to the core were dislodged by the drones. The Fukushima disaster was one of the world's most devastating nuclear mishaps The plant's operators, Tokyo Electric Power Company Holdings, or TEPCO, released 12 photos from inside the site TEPCO officials said they were unable to tell from the images whether the dangling lumps were melted fuel or melted equipment without obtaining other data such as radiation levels.

Document-level Event Causality Identification (DECI) aims to identify causal relations between two events in documents. Recent research tends to use pre-trained language models to generate the event causal relations. Whereas, these methods are prone to the errors of sequential generation due to multiple events in a document. Moreover, the potential structures such as event coreference and related causal chain are neglected. In this paper, we propose a multi-task learning framework to enhance event causality identification with rationale and structure-aware causal question answering. Specifically, the DECI task is transformed into multiple-choice question answering, and the causes and effects of the questioned event are generated with large language models. In addition, we generate the rationales to explain why these events have causal relations. Moreover, we construct an event structure graph, which models the multi-hop potential relations for causal reasoning of the current event. Experiments on two benchmark datasets show the great advantages of our proposed approach compared to the state-of-the-art methods. Moreover, we conduct both quantitative and qualitative analyses, which shed light on why each component of our approach can lead to great improvements.

False information concerning a powerful earthquake in central Japan on New Year's Day has been spreading on social media platforms, prompting the government on Wednesday to call for the public to exercise caution. Some posts have attributed the cause of the magnitude-7.6 A woman in her 40s in one of the disaster-hit areas had her address publicly disclosed in a post attributed to her that stated, "My son is trapped and cannot move." She said, "This is false information and my home's location is now known. I want the post to be removed."

If you're waiting for the hubbub over generative AI to die down, maybe pull up a chair. The buzz around artificial intelligence shows no signs of quieting--a fact that will become all too obvious at this year's CES. CES, the consumer electronics industry's largest annual gathering in the US, is returning to Las Vegas on January 9. It is a massive, four-day-long bustling bazaar of tech, with expo halls filled to the brim with new gadgets, hopeful startups, and prototypes that reach for the stars. CES is a trade show where sales and distribution deals are inked, where concept cars roll through crowded streets, and where tech journalists and showgoers alike wander the floors looking for the standout new products.

Before the 2011 tsunami inundated Ukedo elementary school's classrooms, the ocean was central to the school's identity. In the summer, pupils would run down the 300-metre path to the beach, splitting up into groups to see who could make the best animals out of sand. Every year, students also painted local fishermen's boats, a tradition that resonated strongly in Namie town, where many parents worked in the fishing industry. But when a magnitude 9.0 earthquake, a subsequent tsunami and a nuclear disaster brought devastation to Japan's northeastern Tohoku region, that all changed, Shinichi Sato, a teacher who taught at Ukedo elementary school, told Al Jazeera. "For years after the disaster, we weren't allowed to teach lessons outside, in fear that kids would touch radioactive soil," Sato said.

A better tsunami early warning system could become a reality with the help of AI. Researchers have shown how widely-available AI technology can detect the subtle disturbances in the atmosphere caused when a tsunami's destructive waves begin to form far out to sea – a demonstration that could help provide earlier warnings for coastal communities long before the tsunami reaches them. "There is no global network for detecting tsunami waves, and installing physical hardware, like buoy-based systems, …

It looks like 2024 will be a pivotal year for democracy. There are elections taking place all over the free world – in South Africa, Ghana, Tunisia, Mexico, India, Austria, Belgium, Lithuania, Moldova and Slovakia, to name just a few. Of these, the last may be the most pivotal because: Donald Trump is a racing certainty to be the Republican candidate; a significant segment of the voting population seems to believe that the 2020 election was "stolen"; and the Democrats are, well… underwhelming. The consequences of a Trump victory would be epochal. It would mean the end (for the time being, at least) of the US experiment with democracy, because the people behind Trump have been assiduously making what the normally sober Economist describes as "meticulous, ruthless preparations" for his second, vengeful term.

Tsunamis can trigger internal gravity waves (IGWs) in the ionosphere, perturbing the Total Electron Content (TEC) - referred to as Traveling Ionospheric Disturbances (TIDs) that are detectable through the Global Navigation Satellite System (GNSS). The GNSS are constellations of satellites providing signals from Earth orbit - Europe's Galileo, the United States' Global Positioning System (GPS), Russia's Global'naya Navigatsionnaya Sputnikovaya Sistema (GLONASS) and China's BeiDou. The real-time detection of TIDs provides an approach for tsunami detection, enhancing early warning systems by providing open-ocean coverage in geographic areas not serviceable by buoy-based warning systems. Large volumes of the GNSS data is leveraged by deep learning, which effectively handles complex non-linear relationships across thousands of data streams. We describe a framework leveraging slant total electron content (sTEC) from the VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm by Gramian Angular Difference Fields (from Computer Vision) and Convolutional Neural Networks (CNNs) to detect TIDs in near-real-time. Historical data from the 2010 Maule, 2011 Tohoku and the 2012 Haida-Gwaii earthquakes and tsunamis are used in model training, and the later-occurring 2015 Illapel earthquake and tsunami in Chile for out-of-sample model validation. Using the experimental framework described in the paper, we achieved a 91.7% F1 score. Source code is available at: https://github.com/vc1492a/tidd. Our work represents a new frontier in detecting tsunami-driven IGWs in open-ocean, dramatically improving the potential for natural hazards detection for coastal communities.