An elephant foot bone found by archaeologists digging in southern Spain may be evidence that a troop of war elephants stomped through ancient Europe. It would be the first concrete proof of the legendary Carthaginian General Hannibal's troop of battle elephants, according to academics. Drawings of Hannibal's war against the Romans had long suggested that the beasts were used in fighting, but no hard evidence backed up the theories. Now the creatures' skeletal remains appear to have been found in an Iron Age dig near Cordoba. Beyond ivory, the discovery of elephant remains in European archaeological contexts is exceptionally rare, says the team of scientists in a paper published in Journal of Archaeological Science: Reports.

The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.

Science Archaeology Ancient'dirty dishes' may have led archaeologists astray for decades A new study questions if Bronze Age dishes really do have traces of olive oil. Breakthroughs, discoveries, and DIY tips sent every weekday. As far as kitchen staples, you don't really get much better than olive oil . It can do it all--jazz up a salad, sauté vegetables, add a nice crisp to some noodles, and more. Humans have been using olive oil for about 8,000 years, so archeologists often report olive oil residue on excavated pottery.

Because of shifting storms and sweltering summers, Iran's capital faces a future "Day Zero" when the taps run dry. During the summer of 2025, Iran experienced an exceptional heat wave, with daytime temperatures across several regions, including Tehran, approaching 50 degrees Celsius (122 degrees Fahrenheit) and forcing the temporary closure of public offices and banks. During this period, major reservoirs supplying the Tehran region reached record-low levels, and water supply systems came under acute strain . By early November, the reservoir behind Amir Kabir Dam, a main source of drinking water for Tehran, had dropped to about 8 percent of its capacity . The present crisis reflects not only this summer's extreme heat but also several consecutive years of reduced precipitation and ongoing drought conditions across Iran.

The vast amount of data required to model wildfires at a larger scale presents difficulties in the collection and curation of the data.

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track.

An ancient ship containing hundreds of stunningly-preserved wine jugs has been found on the floor of the Mediterranean. The 40-foot vessel, found 1 mile deep on the seafloor 56 miles from Israel's coast, dates back 3,300 years to the late Bronze Age, experts say. It's thought to be the oldest ship found this deep in the Med, as previous shipwrecks from this era never ventured this far away from land. This suggests ancient seafarers were more capable at navigating the deep seas than historians previously thought. The ship likely sunk either from a storm or after coming under attack by pirates, the discoverers believe.

Wildfire forecasting is notoriously hard due to the complex interplay of different factors such as weather conditions, vegetation types and human activities. Deep learning models show promise in dealing with this complexity by learning directly from data. However, to inform critical decision making, we argue that we need models that are right for the right reasons; that is, the implicit rules learned should be grounded by the underlying processes driving wildfires. In that direction, we propose integrating causality with Graph Neural Networks (GNNs) that explicitly model the causal mechanism among complex variables via graph learning. The causal adjacency matrix considers the synergistic effect among variables and removes the spurious links from highly correlated impacts. Our methodology's effectiveness is demonstrated through superior performance forecasting wildfire patterns in the European boreal and mediterranean biome. The gain is especially prominent in a highly imbalanced dataset, showcasing an enhanced robustness of the model to adapt to regime shifts in functional relationships. Furthermore, SHAP values from our trained model further enhance our understanding of the model's inner workings.

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.