A significant impetus for the negotiation of the United Nations Convention on the Law of the Sea (UNCLOS) was the impact of new technological and scientific developments on the law of the sea. Such developments have continued apace, raising the question of how UNCLOS continues to respond to new uses of, and threats to, the oceans. This article focuses on marine geoengineering as an emerging technological response to the climate emergency and its regulation by the specialised global dumping regime of the Convention on the Prevention of Marine Pollution by Dumping of Wastes and Other Matter and its Protocol within the general normative framework provided by UNCLOS. It demonstrates how responding to technological developments is hard-wired into the DNA of the law of the sea, and into UNCLOS in particular, which remains the foundation for the governance and management of new maritime technologies.

We present Evolutionary Map of the Universe Search Engine (EMUSE), a tool designed for searching specific radio sources within the extensive datasets of the Evolutionary Map of the Universe (EMU) survey, with potential applications to other Big Data challenges in astronomy. Built on a multimodal approach to radio source classification and retrieval, EMUSE fine-tunes the OpenCLIP model on curated radio galaxy datasets. Leveraging the power of foundation models, our work integrates visual and textual embeddings to enable efficient and flexible searches within large radio astronomical datasets. We fine-tune OpenCLIP using a dataset of 2 900 radio galaxies, encompassing various morphological classes, including FR-I, FR-II, FR-x, R-type, and other rare and peculiar sources. The model is optimised using adapter-based fine-tuning, ensuring computational efficiency while capturing the unique characteristics of radio sources. The fine-tuned model is then deployed in the EMUSE, allowing for seamless image and text-based queries over the EMU survey dataset. Our results demonstrate the model’s effectiveness in retrieving and classifying radio sources, particularly in recognising distinct morphological features. However, challenges remain in identifying rare or previously unseen radio sources, highlighting the need for expanded datasets and continuous refinement. This study showcases the potential of multimodal machine learning in radio astronomy, paving the way for more scalable and accurate search tools in the field. The search engine is accessible at https://askap-emuse.streamlit.app/ and can be used locally by cloning the repository at https://github.com/Nikhel1/EMUSE.

At present, the international situation is complex and volatile, with the global economy facing multiple risks and challenges. The North–South development divide is widening, drawing increasing attention to the “Global South.” Given their distinct historical backgrounds and resource characteristics, countries in the Global South cannot simply replicate the development models of developed nations; rather, they must formulate their own development strategies tailored to their specific national conditions. The development models of these countries vary significantly and warrant further exploration.