The Dutch ship'Dom van Keulen' sank off the coast of England in 1633. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Gold coins and jewelry recovered from the wreck of the'Dom van Keulen.' Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Researchers have officially solved a shipwreck mystery that has plagued historians for nearly 30 years--the identity of a ship discovered off the southern coast of England in 1995.

We propose a Koopman-enhanced physics-informed neural network (K--PINN) framework for parameter inference and forecasting in nonlinear epidemic models. This method combines Koopman operator theory and physics-informed learning. It maps epidemic states into a latent observable space where the dynamics evolve approximately linearly while satisfying the governing epidemic equations through automatic differentiation. This integration improves interpretability, parameter identifiability, and long-term predictive stability. We apply the proposed framework to a normalized SEIRSD epidemic model and evaluate it using synthetic monkeypox (Mpox) data and real-world datasets from Germany, Morocco, and Sweden for the SARS-CoV-2 virus. Synthetic trajectories are generated using a structure-preserving, nonstandard finite difference scheme to ensure reliable training data. Numerical results demonstrate that K--PINN achieves more accurate parameter estimation, trajectory reconstruction, and long-term forecasting than classical PINNs and Koopman-EDMD approaches. These results suggest that K--PINN is an effective machine learning framework for epidemic modeling that can be extended to more complex systems.

Africa Cup of Nations: Who will win the AFCON final. After weeks of drama, it all comes down to this. And 90 minutes to decide who writes the final chapter. Samantha Johnson looks ahead to the AFCON final. Jake Paul vs Anthony Joshua: Is this good for Boxing?

This paper presents Awal, a community-powered initiative for developing language technology resources for Tamazight. We provide a comprehensive review of the NLP landscape for Tamazight, examining recent progress in computational resources, and the emergence of community-driven approaches to address persistent data scarcity. Launched in 2024, awaldigital.org platform addresses the underrepresentation of Tamazight in digital spaces through a collaborative platform enabling speakers to contribute translation and voice data. We analyze 18 months of community engagement, revealing significant barriers to participation including limited confidence in written Tamazight and ongoing standardization challenges. Despite widespread positive reception, actual data contribution remained concentrated among linguists and activists. The modest scale of community contributions -- 6,421 translation pairs and 3 hours of speech data -- highlights the limitations of applying standard crowdsourcing approaches to languages with complex sociolinguistic contexts. We are working on improved open-source MT models using the collected data.

Game Theory: Is Moroccan football in its Golden Era? For decades, football's talent pipeline has flowed from Africa to Europe. But Morocco is reversing that trend. Samantha Johnson looks at how Morocco's football ecosystem can challenge football's traditional hierarchy. What's behind bans on away fans?

Former Israeli Amb. to the U.S. Michael Oren discusses Iran's nuclear capabilities and negotiations with Hamas to release more hostages on'Fox Report.' The idea that Middle East peace cannot and should not advance without a formal agreement between Israel and the Palestinian Authority is outdated and demonstrably untrue. Indeed, it has done little but exacerbate conflict over the last 30 years and undermine U.S. interests in the region. They provide a new paradigm for peace between Israel and all of its neighbors, including the Palestinians. President Donald Trump obviously deserves the Nobel Peace Prize for breaking with the failed Oslo peace process paradigm still sanctified by legacy media and a bipartisan community of foreign policy elites, and for building new bridges of mutually-beneficial cooperation between Israel and its Arab neighbors.

Wildfires pose significant threats to ecosystems, economies, and communities worldwide, necessitating advanced predictive methods for effective mitigation. This study introduces a novel and comprehensive dataset specifically designed for wildfire prediction in Morocco, addressing its unique geographical and climatic challenges. By integrating satellite observations and ground station data, we compile essential environmental indicators such as vegetation health (NDVI), population density, soil moisture levels, and meteorological data aimed at predicting next-day wildfire occurrences with high accuracy. Our methodology incorporates state-of-the-art machine learning and deep learning algorithms, demonstrating superior performance in capturing wildfire dynamics compared to traditional models. Preliminary results show that models using this dataset achieve an accuracy of up to 90%, significantly improving prediction capabilities. The public availability of this dataset fosters scientific collaboration, aiming to refine predictive models and develop innovative wildfire management strategies. Our work not only advances the technical field of dataset creation but also emphasizes the necessity for localized research in underrepresented regions, providing a scalable model for other areas facing similar environmental challenges.

Tnirte, Morocco – Abdel Abed is watching the other villagers digging. When one of them gets tired, he scrambles down and takes over. It has been five days since the magnitude 6.8 earthquake ripped through the mountainous regions around Marrakesh, Morocco, and Abed's daughter, nine-year-old Shaima, is still buried under the rocks. Abed still hopes she may be alive, a family member explains, and he works with almost robotic energy as excavation efforts continue in Tnirte in the High Atlas Mountains. His wife was pulled dead from the rocks yesterday.

World Cup 2022 produced incredible football. At the start of the tournament, Al Jazeera introduced Kashef, our artificial intelligence (AI) robot, to crunch the numbers and predict the results of each game. After every day of action, Kashef downloaded the day's data and compared it with more than 200 metrics, including the number of wins, goals scored and FIFA rankings, from matches played over the past century, totalling more than 100,000 records, to see who was most likely to win the following day. The group stages from November 20 to December 2 were not very kind to Kashef, who erred on the side of caution and failed to foresee any of the many major upsets. The good news for us sentient beings is that every time Kashef got it wrong, we were treated to a feast of World Cup magic, including Saudi Arabia's stunning 2-1 victory over Argentina, Morocco's 2-0 defeat of Belgium and Tunisia's 1-0 win over 2018 champions France.

Machine learning (ML) methods can effectively analyse data, recognize patterns in them, and make high-quality predictions. Good predictions usually come along with "black-box" models that are unable to present the detected patterns in a human-readable way. Technical developments recently led to eXplainable Artificial Intelligence (XAI) techniques that aim to open such black-boxes and enable humans to gain new insights from detected patterns. We investigated the application of XAI in an area where specific insights can have a significant effect on consumer behaviour, namely electricity use. Knowing that specific feedback on individuals' electricity consumption triggers resource conservation, we created five visualizations with ML and XAI methods from electricity consumption time series for highly personalized feedback, considering existing domain-specific design knowledge. Our experimental evaluation with 152 participants showed that humans can assimilate the pattern displayed by XAI visualizations, but such visualizations should follow known visualization patterns to be well-understood by users.