Automatic Term Extraction (ATE) identifies domain-specific expressions that are crucial for downstream tasks such as machine translation and information retrieval. Although large language models (LLMs) have significantly advanced various NLP tasks, their potential for ATE has scarcely been examined. We propose a retrieval-based prompting strategy that, in the few-shot setting, selects demonstrations according to \emph{syntactic} rather than semantic similarity. This syntactic retrieval method is domain-agnostic and provides more reliable guidance for capturing term boundaries. We evaluate the approach in both in-domain and cross-domain settings, analyzing how lexical overlap between the query sentence and its retrieved examples affects performance. Experiments on three specialized ATE benchmarks show that syntactic retrieval improves F1-score. These findings highlight the importance of syntactic cues when adapting LLMs to terminology-extraction tasks.

Advances in the field of computer science, such as very complex simulations, data analysis, or machine learning (ML) in data-driven applications (for example, computational fluid dynamics, large language models) are leading to an increased demand of IT performance and data storage capacity. Therefore, the electricity demands of digital infrastructures in science and industry are increasing. High-performance computing (HPC) has become an enabling technology and a vital tool to greatly reduce the processing and execution time of advanced computing- or data-intensive tasks. An obvious consequence: HPC datacenters (DCs) require an enormous amount of electricity, have volatile demands, and produce notable amounts of waste heat. If not well located, built, and operated, such infrastructures generate a significant CO2 backpack, and the applications and products that use them inherit the backpack from the computing platform.

This article presents a comprehensive overview of the digital twin technology and its capability levels, with a specific focus on its applications in the wind energy industry. It consolidates the definitions of digital twin and its capability levels on a scale from 0-5; 0-standalone, 1-descriptive, 2-diagnostic, 3-predictive, 4-prescriptive, 5-autonomous. It then, from an industrial perspective, identifies the current state of the art and research needs in the wind energy sector. The article proposes approaches to the identified challenges from the perspective of research institutes and offers a set of recommendations for diverse stakeholders to facilitate the acceptance of the technology. The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.

To this end, vast amounts of data generated by various sources, including sensors and other monitoring systems, need to be effectively structured and represented in a way that can be easily understood and processed by both Artificial Intelligence (AI) systems and humans. The digitalisation of the wind energy sector is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle [2]. The digitalisation process encompasses solutions such as digital twins, decision support systems and AI systems, some of which need to still be developed, in order to contribute to reducing operation and maintenance costs, for increasing the amount of energy delivered, as well as for maximising the efficiency of wind energy systems. In this context, the term Knowledge-Based Systems (KBS) refers to AI systems that formalize knowledge as rules, logical expressions, and conceptualisations [3, 4]. Such systems can be realised as AI-enabled digital twins or decision support systems that rely on databases of knowledge (also referred to as knowledge bases or knowledge graphs), which contain machine-readable facts, rules, and logics about a domain of interest, to assist with problem-solving and decision-making [5].

It is a sparkling, sunny August morning in 2043, as your Air China flight from Beijing touches down gracefully (and almost silently) at LAX. The sleek plane is one of a new generation of hydrogen-powered wide-body jets manufactured by Commercial Aircraft Corp. of China -- the kind of innovation that helped the state-owned company sail past Boeing and Airbus in the 2030s to become the world's largest aerospace group. Starting with the Inflation Reduction Act in 2022, the last two decades have seen massive efforts to clean up transportation all around the United States and throughout the world. Back in the early 2020s, transportation accounted for 29% of America's greenhouse gas emissions, but that number has been steadily dwindling to almost zero -- resulting in cleaner cities everywhere. Not only have electric and hydrogen-powered vehicles replaced gas-guzzling cars, but many people have forsaken car-ownership altogether, in favor of much more economic and widely available solutions like e-bikes, robo-taxis and public transit.

Wind energy has been rapidly gaining popularity as a means for combating climate change. However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers. Battery energy storage systems (BESS) that serve as onsite backup sources are among the solutions to mitigate wind curtailment. However, such an auxiliary role of the BESS might severely weaken its economic viability. This paper addresses the issue by proposing joint wind curtailment reduction and energy arbitrage for the BESS. We decouple the market participation of the co-located wind-battery system and develop a joint-bidding framework for the wind farm and BESS. It is challenging to optimize the joint-bidding because of the stochasticity of energy prices and wind generation. Therefore, we leverage deep reinforcement learning to maximize the overall revenue from the spot market while unlocking the BESS's potential in concurrently reducing wind curtailment and conducting energy arbitrage. We validate the proposed strategy using realistic wind farm data and demonstrate that our joint-bidding strategy responds better to wind curtailment and generates higher revenues than the optimization-based benchmark. Our simulations also reveal that the extra wind generation used to be curtailed can be an effective power source to charge the BESS, resulting in additional financial returns.

Alphabet's Google Cloud empowers organisations to digitally transform themselves into smarter businesses. Its diverse solutions include cloud computing, data analytics, and the latest artificial intelligence (AI) and machine learning tools. Last week, many of the platform's latest advances were shared at Next '22, Google Cloud's annual developer and tech conference about digital transformation in the cloud. We've partnered with Google Cloud over the last few years to apply our AI research for making a positive impact on core solutions used by their customers. Here, we introduce a few of these projects, including optimising document understanding, enhancing the value of wind energy, and offering easier use of AlphaFold.

The last summer showed how warming is a problem we can no longer ignore. Rising global temperatures are causing increasingly extreme events, and the future could be worse. Machine learning and artificial intelligence could help against global warming. In this article, we will try to answer the questions: how? what are currently the applications of artificial intelligence already in the field? Bangladesh and India were hit in June by one of the worst floods ever seen.

Google is bringing artificial intelligence to wind energy. The tech giant is providing wind turbine output prediction software to French utility company Engie to improve efficiency and predictability at Engie's German wind farms. Google Cloud's global energy solutions director compared the technology to a trading recommendations tool, and Google said early tests improved the value of wind energy by 20%. Engie is Google's first customer in this area, and Google says it hopes to expand the service beyond the US$33 billion wind power industry.

The variable and stochastic character of wind energy distinguish it from other renewable resources. As a result, wind energy generation forecasting is critical for power system reliability and balancing supply and demand. This article will look at how machine learning has made wind energy more predictable and recent advancements in this field. Wind energy has gained a lot of attention because of its abundant resources and efficient power-producing technology. However, large-scale strong and uncontrollable wind could undermine the stability of the power grid due to the uncertainty and randomness of the wind.