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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

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Background Quantifying and dealing with lack of consistency in administrative databases (namely, under-coding) requires tracking patients longitudinally without compromising anonymity, which is often a challenging task. Objective This study aimed to (i) assess and compare different hierarchical clustering methods on the identification of individual patients in an administrative database that does not easily allow tracking of episodes from the same patient; (ii) quantify the frequency of potential under-coding; and (iii) identify factors associated with such phenomena. Method We analysed the Portuguese National Hospital Morbidity Dataset, an administrative database registering all hospitalisations occurring in Mainland Portugal between 2011-2015. We applied different approaches of hierarchical clustering methods (either isolated or combined with partitional clustering methods), to identify potential individual patients based on demographic variables and comorbidities. Diagnoses codes were grouped into the Charlson an Elixhauser comorbidity defined groups. The algorithm displaying the best performance was used to quantify potential under-coding. A generalised mixed model (GML) of binomial regression was applied to assess factors associated with such potential under-coding. Results We observed that the hierarchical cluster analysis (HCA) + k-means clustering method with comorbidities grouped according to the Charlson defined groups was the algorithm displaying the best performance (with a Rand Index of 0.99997). We identified potential under-coding in all Charlson comorbidity groups, ranging from 3.5% (overall diabetes) to 27.7% (asthma). Overall, being male, having medical admission, dying during hospitalisation or being admitted at more specific and complex hospitals were associated with increased odds of potential under-coding. Discussion We assessed several approaches to identify individual patients in an administrative database and, subsequently, by applying HCA + k-means algorithm, we tracked coding inconsistency and potentially improved data quality. We reported consistent potential under-coding in all defined groups of comorbidities and potential factors associated with such lack of completeness. Conclusion Our proposed methodological framework could both enhance data quality and act as a reference for other studies relying on databases with similar problems.

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Angle assessment is crucial in rehabilitation and significantly influences physiotherapists' decision-making. Although visual inspection is commonly used, it is known to be approximate. This work aims to be a preliminary study about using the AI image-based to assess upper limb joint angles. Two main frameworks were evaluated: MediaPipe and Yolo v7. The study was performed with 28 participants performing four upper limb movements. The results showed that Yolo v7 achieved greater estimation accuracy than Mediapipe, with MAEs of around 5 degrees and 17 degrees, respectively. However, even with better results, Yolo v7 showed some limitations, including the point of detection in only a 2D plane, the higher computational power required to enable detection, and the difficulty of performing movements requiring more than one degree of Freedom (DOF). Nevertheless, this study highlights the detection capabilities of AI approaches, showing be a promising approach for measuring angles in rehabilitation activities, representing a cost-effective and easy-to-implement solution.