Scientists at China's Beihang University developed a tiny morphable robot to explore the ocean's depths -- and it's now taken a dive into the Mariana Trench. The team successfully demonstrated that its creation can swim, crawl, and glide untethered at a depth of 10,600 meters (34,776 feet) in Earth's deepest oceanic trench, located in the Pacific Ocean. Separately, the researchers also developed a soft gripper, which can be attached to a rigid robot. It was tested in the South China Sea, where the team attached it to an arm on a submersible, which they sent 3,400 metres (34,776 feet) deep. There, the gripper collected small marine creatures, such as starfish and urchins resting on the seafloor.

Far above your head, constellations of satellites are working constantly to provide the positioning, navigation, and timing systems that quietly run modern life. Known as the global navigation satellite system, or GNSS, signals from these satellites provide the foundation for mobile networks, energy grids, the internet, and GPS. And increasingly, their dependability is under threat. GPS signals can be jammed--deliberately drowned out with other powerful radio signals--and spoofed, where erroneous signals are released to fool positioning systems. GPS interference has been documented in Ukraine, the Middle East, and the South China Sea.

It took over 15 years and more than 1,000 remotely operated vehicle (ROV) expeditions, but researchers aboard the NOAA Ocean Exploration Trust's Nautilus finally spotted their research vessel's namesake in the wild. On December 3, operators of the ship's Hercules ROV located four specimens of Palau nautilus (Nautilus belauensis) during the Nautilus Exploration Program's ongoing, 17-day survey in the Palau National Marine Sanctuary. While the team recorded these particular examples swimming 220-to-375 meters (roughly 721-to-1,230 ft) below the Pacific Ocean's surface, the pelagic marine mollusk cephalopods can survive at depths approaching 2,500 feet. Their spiral-shelled bodies belong to one of Earth's oldest families of animals, with fossil records indicating the squid relatives have changed comparatively little even after nearly 500 million years. Although their sight is limited due to rudimentary eyes that lack solid lenses, nine known nautilus species instead rely heavily on their olfactory senses to find food and mates.

Forecasting sea surface currents is essential for applications such as maritime navigation, environmental monitoring, and climate analysis, particularly in regions like the Gulf of Thailand and the Andaman Sea. This paper introduces SEA-ViT, an advanced deep learning model that integrates Vision Transformer (ViT) with bidirectional Gated Recurrent Units (GRUs) to capture spatio-temporal covariance for predicting sea surface currents (U, V) using high-frequency radar (HF) data. The name SEA-ViT is derived from ``Sea Surface Currents Forecasting using Vision Transformer,'' highlighting the model's emphasis on ocean dynamics and its use of the ViT architecture to enhance forecasting capabilities. SEA-ViT is designed to unravel complex dependencies by leveraging a rich dataset spanning over 30 years and incorporating ENSO indices (El Ni\~no, La Ni\~na, and neutral phases) to address the intricate relationship between geographic coordinates and climatic variations. This development enhances the predictive capabilities for sea surface currents, supporting the efforts of the Geo-Informatics and Space Technology Development Agency (GISTDA) in Thailand's maritime regions. The code and pretrained models are available at \url{https://github.com/kaopanboonyuen/gistda-ai-sea-surface-currents}.

The observation of the advancing and retreating pattern of polar sea ice cover stands as a vital indicator of global warming. This research aims to develop a robust, effective, and scalable system for classifying polar sea ice as thick/snow-covered, young/thin, or open water using Sentinel-2 (S2) images. Since the S2 satellite is actively capturing high-resolution imagery over the earth's surface, there are lots of images that need to be classified. One major obstacle is the absence of labeled S2 training data (images) to act as the ground truth. We demonstrate a scalable and accurate method for segmenting and automatically labeling S2 images using carefully determined color thresholds. We employ a parallel workflow using PySpark to scale and achieve 9-fold data loading and 16-fold map-reduce speedup on auto-labeling S2 images based on thin cloud and shadow-filtered color-based segmentation to generate label data. The auto-labeled data generated from this process are then employed to train a U-Net machine learning model, resulting in good classification accuracy. As training the U-Net classification model is computationally heavy and time-consuming, we distribute the U-Net model training to scale it over 8 GPUs using the Horovod framework over a DGX cluster with a 7.21x speedup without affecting the accuracy of the model. Using the Antarctic's Ross Sea region as an example, the U-Net model trained on auto-labeled data achieves a classification accuracy of 98.97% for auto-labeled training datasets when the thin clouds and shadows from the S2 images are filtered out.

The number of humpback whales in the North Pacific Ocean fell by 20 per cent between 2012 and 2021, according to a study that used artificial intelligence to identify individual whales from photos of their tails. The decline coincided with a massive marine heatwave sometimes called the blob, which began in 2013 and lasted until 2016. The unprecedented intensity of the blob was almost certainly the result of global warming. The findings suggest that around 7000 whales starved to death because of the marine heatwave, says Ted Cheeseman at Southern Cross University in Australia. The blob is known to have caused mass die-offs of many other animals, such as seabirds.

Ocean science, which delves into the oceans that are reservoirs of life and biodiversity, is of great significance given that oceans cover over 70% of our planet's surface. Recently, advances in Large Language Models (LLMs) have transformed the paradigm in science. Despite the success in other domains, current LLMs often fall short in catering to the needs of domain experts like oceanographers, and the potential of LLMs for ocean science is under-explored. The intrinsic reason may be the immense and intricate nature of ocean data as well as the necessity for higher granularity and richness in knowledge. To alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean domain, which is expert in various ocean science tasks. We propose DoInstruct, a novel framework to automatically obtain a large volume of ocean domain instruction data, which generates instructions based on multi-agent collaboration. Additionally, we construct the first oceanography benchmark, OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though comprehensive experiments, OceanGPT not only shows a higher level of knowledge expertise for oceans science tasks but also gains preliminary embodied intelligence capabilities in ocean technology. Codes, data and checkpoints will soon be available at https://github.com/zjunlp/KnowLM.

TRopIcal DEep-sea Neutrino Telescope (TRIDENT) is a next-generation neutrino telescope to be located in the South China Sea. With a large detector volume and the use of advanced hybrid digital optical modules (hDOMs), TRIDENT aims to discover multiple astrophysical neutrino sources and probe all-flavor neutrino physics. The reconstruction resolution of primary neutrinos is on the critical path to these scientific goals. We have developed a novel reconstruction method based on graph neural network (GNN) for TRIDENT. In this paper, we present the reconstruction performance of the GNN-based approach on both track- and shower-like neutrino events in TRIDENT.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Philippine defense chief has ordered all defense personnel and the 163,000-member military to refrain from using digital applications that harness artificial intelligence to generate personal portraits, saying they could pose security risks. Defense Secretary Gilberto Teodoro Jr. issued the order in an Oct. 14 memorandum, as Philippine forces have been working to weaken decades-old communist and Muslim insurgencies and defend territorial interests in the disputed South China Sea. The Department of National Defense on Friday confirmed the authenticity of the memo, which has been circulating online in recent days, but did not provide other details, including what prompted Teodoro to issue the prohibition.

When the Titan submersible, carrying five sightseers to the wreck of the Titanic, blew up thousands of metres under the ocean surface in June, it underscored why humanity knows more about the surface of some other planets than about the depths of the Earth's oceans. Oceans cover more than 70 percent of the earth's surface. Yet, this underwater world is a challenging place to explore, as the Titan disaster showed. The deepest point under water, the Challenger Deep in the Pacific Ocean, is 11,000 metres deep, more than the height of Mount Everest. The light doesn't penetrate to such depths.