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This paper introduces a probabilistic ensemble model based on a conditional diffusion model for real-time tsunami inundation forecasting, offering uncertainty quantification in contrast to deterministic warnings. Validated with 2011 Tohoku-oki data, it demonstrates that generative AI can shift tsunami forecasting from deterministic to probabilistic approaches.
Article discusses new firefighting technologies, including a biodegradable fire suppressant (Ecofire), AI-driven early-warning systems (Predifire), wireless sensor networks (SenForFire), and an AI wildfire assessment model, all aimed at combating increasingly severe wildfires in France and Spain driven by climate change.
A second-order early warning signal for multi-turn prompt injection is introduced, based on information geometry and a statistical manifold. The method uses a meta rate derived from the second derivative of the stability parameter to predict adversarial trajectories before threshold crossing, providing proactive detection.
This paper presents a multimodal NLP framework that fuses XLM-RoBERTa and CLIP with geospatial and sarcasm features to detect fake news and predict violence-driven mob activity, achieving 98% test accuracy on a 138,256-sample Bangla/English dataset.
Google's Android earthquake alert system uses smartphone accelerometers to detect P-waves and sends alerts via internet faster than damaging S-waves, providing users with seconds of warning before strong shaking arrives.
This paper introduces SpatioTemporal Causal Network Diagnostics (ST-CND), a framework that uses data-driven causal networks and dynamic mode decomposition to provide localized early warning of geographic tipping points, outperforming classical spatial indicators on synthetic and observational benchmarks.
This paper identifies a powerful space-based GNSS interference source over Europe, Greenland, and Canada as a constellation of Russian early warning satellites in Molniya orbits, based on data from 2019 to 2026.
Applies graph spectral analysis (Fiedler value) and Scheffer critical slowing down indicators to predict grokking in neural networks, detecting it 21,000 steps before the loss function changes, across five reproducible experiments.
Google DeepMind's WeatherNext AI model accurately predicted the intensification of 2025's Hurricane 'Melissa' into a Category 5 hurricane and its landfall in Jamaica three days in advance, issuing early warnings that likely saved many lives.