Horrifying next twist in the Alexander brothers case: MAUREEN CALLAHAN exposes an unthinkable perversion that's been hiding in plain sight Alexander brothers' alleged HIGH SCHOOL gang rape video: Classmates speak out on sick'taking turns' footage... as creepy unseen photos are exposed Model Cindy Crawford, 60, mocked for her'out of touch' morning routine: 'Nothing about this is normal' Kentucky mother and daughter turn down $26.5MILLION to sell their farms to secretive tech giant that wants to build data center there Tucker Carlson erupts at Trump adviser as she hurls'SLANDER' claim linking him to synagogue shooting NFL superstar Xavier Worthy spills all on Travis Kelce, the Chiefs' struggles... and having Taylor Swift as his No 1 fan Heartbreaking video shows very elderly DoorDash driver shuffle down customer's driveway with coffee order because he is too poor to retire Amber Valletta, 52, was a '90s Vogue model who made movies with Sandra Bullock and Kate Hudson, see her now Nancy Mace throws herself into Iran warzone as she goes rogue on Middle East rescue mission: 'I AM that person' Hidden toxins in kids' treats EXPOSED: Health guru Jillian Michaels' sit-down with Casey DeSantis reveals dangers lurking in popular foods Scientists confirm biblical earthquake that shook the earth at the moment of Jesus' crucifixion A decade-old study claiming to find evidence of the earthquake described in the Bible at the time of Jesus's crucifixion is reigniting debate after resurfacing online. The Gospel of Matthew says'the earth shook' moments after Jesus cried out before dying on the cross, and researchers in 2012 reported evidence that could support the verse. A team of geologists examined sediment layers near the Dead Sea, about 25 miles from where many scholars believe the crucifixion took place. Their analysis revealed signs of at least two significant earthquakes affecting the region. Disturbances in the sediment pointed to a major quake around 31 BC and a smaller seismic event sometime between 26 and 36 AD.

By making our data processing source code publiclyavailable, weaim toengage themarine science community toenrich thedata pool andinspire themachine learning community to develop more robust models.

The elusive'snot otters' can grow up to two feet long. Breakthroughs, discoveries, and DIY tips sent every weekday. The Eastern hellbender () isn't nearly as fearsome as its name implies. They're actually somewhat cute, if you can get past the salamander's slimy, mucousy skin that's earned it such nicknames, such as "snot otter" and "lasagna lizard." Although hellbenders can grow up to two feet long, the amphibians are notoriously elusive and prefer to reside under large, flat rocks in well-oxygenated waterways that snake through Appalachia and the Ohio River basin.

A new study finds algae, mussels, and starfish flock to munitions dumped in the Baltic Sea. Breakthroughs, discoveries, and DIY tips sent every weekday. As the ink dried on Germany's unconditional surrender on May 8, 1945, celebrations erupted across the world. People cheered, wept, and kissed in the streets as World War II finally came to an end in Europe. A few months later at the Potsdam Conference, Germany agreed to demilitarize and dismantle its once formidable army, leaving the nation with lots and lots of leftover munitions.

Detecting heavy metal pollution in soils and seaports is vital for regional environmental monitoring. The Pollution Load Index (PLI), an international standard, is commonly used to assess heavy metal containment. However, the conventional PLI assessment involves laborious procedures and data analysis of sediment samples. To address this challenge, we propose a deep-learning-based model that simplifies the heavy metal assessment process. Our model tackles the issue of data scarcity in the water-sediment domain, which is traditionally plagued by challenges in data collection and varying standards across nations. By leveraging transfer learning, we develop an accurate quantitative assessment method for predicting PLI. Our approach allows the transfer of learned features across domains with different sets of features. We evaluate our model using data from six major ports in New South Wales, Australia: Port Yamba, Port Newcastle, Port Jackson, Port Botany, Port Kembla, and Port Eden. The results demonstrate significantly lower Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) of approximately 0.5 and 0.03, respectively, compared to other models. Our model performance is up to 2 orders of magnitude than other baseline models. Our proposed model offers an innovative, accessible, and cost-effective approach to predicting water quality, benefiting marine life conservation, aquaculture, and industrial pollution monitoring.

Breakthroughs, discoveries, and DIY tips sent every weekday. A pink flamingo is typically associated with a laid back lifestyle, but the way that these leggy birds with big personalities feed is anything but chill. When they dip their curved necks into the water, the birds use their feet, heads, and beaks to create swirling water tornadoes to efficiently group their prey together and slurp up them up. The findings are detailed in a study published this week in the journal Proceedings of the National Academy of Sciences (PNAS). "Flamingos are actually predators, they are actively looking for animals that are moving in the water, and the problem they face is how to concentrate these animals, to pull them together and feed," Victor Ortega Jiménez, a study co-author and biologist specializing in biomechanics at the University of California, Berkeley, said in a statement.

Pre-training foundation models has become the de-facto procedure for deep learning approaches, yet its application remains limited in the geological studies, where in needs of the model transferability to break the shackle of data scarcity. Here we target on the X-ray fluorescence (XRF) scanning data, a standard high-resolution measurement in extensive scientific drilling projects. We propose a scalable self-supervised learner, masked autoencoders on XRF spectra (MAX), to pre-train a foundation model covering geological records from multiple regions of the Pacific and Southern Ocean. In pre-training, we find that masking a high proportion of the input spectrum (50\%) yields a nontrivial and meaningful self-supervisory task. For downstream tasks, we select the quantification of XRF spectra into two costly geochemical measurements, CaCO$_3$ and total organic carbon, due to their importance in understanding the paleo-oceanic carbon system. Our results show that MAX, requiring only one-third of the data, outperforms models without pre-training in terms of quantification accuracy. Additionally, the model's generalizability improves by more than 60\% in zero-shot tests on new materials, with explainability further ensuring its robustness. Thus, our approach offers a promising pathway to overcome data scarcity in geological discovery by leveraging the self-supervised foundation model and fast-acquired XRF scanning data.

The geotechnical evaluation of seabed sediments is important for engineering projects and naval applications, offering valuable insights into sediment properties, behavior, and strength. Obtaining high-quality seabed samples can be a challenging task, making in-situ testing an essential part of site characterization. Free Fall Penetrometers (FFP) have emerged as robust tools for rapidly profiling seabed surface sediments, even in energetic nearshore or estuarine conditions and shallow as well as deep depths. While methods for interpretation of traditional offshore Cone Penetration Testing (CPT) data are well-established, their adaptation to FFP data is still an area of research. In this study, we introduce an innovative approach that utilizes machine learning algorithms to create a sediment behavior classification system based on portable free fall penetrometer (PFFP) data. The proposed model leverages PFFP measurements obtained from locations such as Sequim Bay (Washington), the Potomac River, and the York River (Virginia). The result shows 91.1\% accuracy in the class prediction, with the classes representing cohesionless sediment with little to no plasticity, cohesionless sediment with some plasticity, cohesive sediment with low plasticity, and cohesive sediment with high plasticity. The model prediction not only provides the predicted class but also yields an estimate of inherent uncertainty associated with the prediction, which can provide valuable insight about different sediment behaviors. These uncertainties typically range from very low to very high, with lower uncertainties being more common, but they can increase significantly dpending on variations in sediment composition, environmental conditions, and operational techniques. By quantifying uncertainty, the model offers a more comprehensive and informed approach to sediment classification.

The Mars rover Perseverance captured a dust devil moving across the rim of a crater. A robot chemist powered by artificial intelligence could solve the puzzle of providing oxygen to humans on Mars, according to the results of a new study. The study, published in Nature Synthesis, found that an AI robot could quickly figure out how to cook up vital oxygen for survival compared to humans, who would take a lifetime to complete such a task. The reason, according to the paper, is there are more than a million potential oxygen evolution reaction (OER) catalysts on Mars, which would give humans too many possibilities to work with when trying to create oxygen. Adding to the problem would be communication with Earth to solve the problems, with transmissions taking as long as 20 minutes to travel between the home planet and Mars.

In areas that are inaccessible to humans, such as the lunar surface and landslide sites, there is a need for multiple autonomous mobile robot systems that can replace human workers. In particular, at landslide sites such as river channel blockages, robots are required to remove water and sediment from the site as soon as possible. Conventionally, several construction machines have been deployed to the site for civil engineering work. However, because of the large size and weight of conventional construction equipment, it is difficult to move multiple units of construction equipment to the site, resulting in significant transportation costs and time. To solve such problems, this study proposes a novel growing robot by eating environmental material called GREEMA, which is lightweight and compact during transportation, but can function by eating on environmental materials once it arrives at the site. GREEMA actively takes in environmental materials such as water and sediment, uses them as its structure, and removes them by moving itself. In this paper, we developed and experimentally verified two types of GREEMAs. First, we developed a fin-type swimming robot that passively takes water into its body using a water-absorbing polymer and forms a body to express its swimming function. Second, we constructed an arm-type robot that eats soil to increase the rigidity of its body. We discuss the results of these two experiments from the viewpoint of Explicit-Implicit control and describe the design theory of GREEMA.