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Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth.

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This research assesses the behaviour of alternative objectives related to maximising the energy self-consumed in renewable energy communities. Three different objective functions are proposed: minimising the grid-supplied energy to the community members, reducing the energy surplus of the community injected into the grid, and maximising the self-consumed energy according to its definition in the Portuguese regulation. Two additional objectives were also considered for comparison purposes, the maximisation of the equivalent CO2 emissions saved and the minimisation of the total community energy cost. The methodology involves formulating and implementing the optimisation problems and discussing the results with a case example, including decreased grid dependency, utilisation of battery storage, and differences in energy trading strategies within the REC. Overall, this research contributes to understanding some alternative objectives that could be considered for the management of the flexible resources of a REC. © The Institution of Engineering & Technology 2024.

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The Mid-Infrared ELT Imager and Spectrograph (METIS) will be one of only three 1st-generation science instruments on the 39m Extremely Large Telescope (ELT). METIS will provide diffraction-limited imaging and medium resolution slit-spectroscopy from 3-13 microns (L, M, and N bands), as well as high resolution (R approximate to 100,000) integral field spectroscopy from 2.9-5.3 microns. Both imaging and IFU spectroscopy can be combined with coronagraphic techniques. After the final design reviews of the optics (2021) and the entire system (2022), most hardware procurements have started. In this paper we present an overview of the status of the various ongoing activities. Many hardware components are already in hand, and the manufacturing is in full swing in order to start the assembly and testing of the subsystems in 2024 toward first light at the telescope in 2028/29. This rather brief paper only provides an overview of the project status. For more information, we refer to the detailed instrument paper which will be published soon.

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Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been drawn to OpenAI's GPT models' capabilities and extremely accessible interface. LLMs are nowadays routinely used and studied for downstream tasks and specific applications with great success, pushing forward the state of the art in almost all of them. However, they also exhibit impressive inference capabilities when used off the shelf without further training. In this paper, we aim to study the behavior of pre-trained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as factual probing and common-sense reasoning. We highlight relevant achievements made by these models, as well as some of their current limitations that open opportunities for further research.This article is categorized under:Fundamental Concepts of Data and Knowledge > Key Design Issues in DataMiningTechnologies > Artificial Intelligence