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Item type: Publication , Access status: Open Access , Battery Vessels Could Help Electrify Most of Global Shipping Emissions(2026-11-10) Wiskich, AnthonyBattery-electric ships are economical only if the pack is cycled frequently, so they have been confined to short-sea, ferry and harbour vessels rather than the infrequently-berthing deep-sea fleet. Dedicated battery vessels (BVs) remove this limit by separating the battery from the host: coupling at or near a port and returning to port to recharge, they keep their packs cycling across many hosts. Using a gridded shipping- CO2 field and a geometric relay-corridor servability model, we map the global share BVs could serve. On a denominator of all shipping CO2(e), the battery-addressable share is about 62%, its servable corridors concentrated in European–Mediterranean and East and Southeast Asian waters. Because the coupling traffic hugs the coast, sea state is not the binding constraint. A screening cost model puts BV-delivered energy at breakeven with fuel oil near $173/tCO2e (well-to-wake basis) for a 20MW host and a future $100/kWh battery pack price, falling as host power rises. BVs thus reach the deep-sea fleet’s coastal legs that on-board batteries cannot.Item type: Publication , Access status: Open Access , The Current State of Global Climate Policy: How to Improve Regional Cooperation(2026-11-09) Ishii, Naoko; McKibbin, Warwick; Shuetrim, GeoffreyThis paper provides an update on the current state of climate policies and proposes a policy framework that can improve regional cooperation. Global greenhouse gas emissions will continue to rise to 2050 under current policies, according to projections from the G-Cubed multi-country model. We evaluate an approach to international climate cooperation to substantially reduce greenhouse gas emissions designed to sustain participation: national policies implemented through domestic institutions; a common reference carbon price adjusted for national emissions intensities; free allocation of carbon bonds, which transfers wealth to households and firms while the policy continues; and a safety valve that caps carbon prices at an agreed, negotiated level. This is based on the Hybrid/CALM framework of McKibbin and Wilcoxen (2002b) and McKibbin (2020). We compare three scenarios of halving greenhouse gas emissions from all sectors (except LULUCF) from 2023 emissions by 2050, with carbon pricing beginning in 2027: (i) a uniform national reduction target met with country-specific carbon taxes; (ii) a common US dollar carbon tax in all countries; and (iii) a common reference price adjusted by emissions-intensity multipliers, which illustrates the price differentiation in the Hybrid/CALM approach. Emissions outside the Middle East and North Africa, which is exempt from pricing, fall by about 49 percent from 2023 levels by 2050 in all three scenarios. A Hybrid/CALM mechanism narrows the spread of GDP losses across countries. Relative to a common price, it reduces the 2050 GDP losses in twelve of sixteen regions, with the largest gains in Africa, Indonesia and India, while the United States, Japan and other advanced economies take on more of the adjustment. Many Asian economies also do better than under fixed national targets.Item type: Publication , Access status: Metadata only , LLM as Auto-Prompt Engineer: Automated NER Prompt Optimisation(Association for Computing Machinery (ACM), 2025-05-23) Yang, Can; Nunes, Bernardo Pereira; Méndez, Sergio RodríguezThe emergence of Large Language Models (LLMs) has revolutionised natural language processing capabilities. However, despite these advances, effectively optimising prompts for knowledge extraction tasks like Named Entity Recognition (NER) remains challenging. This paper presents a zero-shot automated prompt engineering approach that decomposes the NER task into two phases: entity boundary detection and entity classification. Our method incorporates structured task analysis, automated prompt generation, test case generation, and iterative optimisation, requiring no labelled training examples. This decomposition allows for more precise entity recognition while maintaining the efficiency. Through experimentation on the CoNLL-2003 dataset using standard exact-match evaluation metrics, our approach demonstrates improvements over unified methods, achieving a 75.39% F1 score compared to baseline approaches (72.90%). The key contributions include: (1) A structured pipeline for zero-shot automated prompt engineering in NER tasks that addresses the challenges of prompt design and optimisation; (2) A two-phase approach to NER tasks that separates boundary detection from entity classification; and (3) Experimental results demonstrating the effectiveness of our approach compared to existing zero-shot approaches in NER tasks.Item type: Publication , Access status: Metadata only , BioNet and NeFF: Crop Biomass Prediction from Point Clouds to Drone Imagery(Institute of Electrical and Electronics Engineers Inc., 2025) Li, Xuesong; Hayder, Zeeshan; Zia, Ali; Cassidy, Connor; Liu, Shiming; Stiller, Warwick; Stone, Eric; Conaty, Warren; Petersson, Lars; Rolland, VivienCrop biomass offers crucial insights into plant health and yield, making it essential for crop science, farming systems, and agricultural research. However, current measurement methods, which are labor-intensive, destructive, and imprecise, hinder large-scale quantification of this trait. To address this limitation, we present a biomass prediction network (BioNet), designed for adaptation across different data modalities, including point clouds and drone imagery. Our BioNet, utilizing a sparse 3D convolutional neural network (CNN) and a transformer-based prediction module, processes point clouds and other 3D data representations to predict biomass. To further extend BioNet for drone imagery, we integrate a neural feature field (NeFF) module, enabling 3D structure reconstruction and the transformation of 2D semantic features from vision foundation models into the corresponding 3D surfaces. For the point cloud modality, BioNet demonstrates superior performance on two public datasets, with an approximate 6.1% relative improvement (RI) over the state-of-the-art. In the RGB image modality, the combination of BioNet and NeFF achieves a 7.9% RI. Additionally, the NeFF-based approach utilizes inexpensive, portable drone-mounted cameras, providing a scalable solution for large field applications.Item type: Publication , Access status: Metadata only , The Diverse Indigenous Creole Languages and First Nations Language Repertoires in Queensland, with Information for Educators(Taylor and Francis, 2025-01-01) Angelo, Denise; Steele, Carly; Yeatman, Bernadine; Yeatman, AllanThe First Nations language landscape on the Australian continent has changed radically since British invasion commenced in 1788, adding Indigenous creole languages and Englishes to the hundreds of traditional languages embedded enduringly in First Nations lands. In Queensland and other parts of northern Australia, Indigenous creole languages are nowadays spoken by many First Nations children and communities as their first and main languages and are a powerful communicative tool in these language ecologies. Indigenous traditional and creole languages express community belonging and represent a continuity of Indigenous identity and a resistance to linguistic assimilation. In this chapter, we introduce the Indigenous languages that are spoken in Queensland today and explain their histories. With a focus on creoles, we describe how these differ from traditional languages and Englishes. We also identify the common patterns of multilingualism involving all these three types of languages. We provide guidance to teachers about important issues for speakers of the Indigenous creole languages in the classroom and educational approaches that address these.