FCT2-Artigos (em revistas ou actas indexadas)
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- Digital twin modelling for a renewable energy community: a case study of the culatra island’s smart gridPublication . Ogunsola, Idris Olalekan; Santos, Joni; Monteiro, Jânio; Pacheco, AndréThis study develops and tests a Digital Twin (DT) of the Culatra Island’s distribution grid to enable the evaluation of demand side management strategies, in the scope of Renewable Energy Communities. Built in MATLAB/Simulink and structured across five functional layers, the DT integrates real-world data from five photovoltaic (PV) production units, monitored and fixed electrical loads, and realistic network parameters derived from the island’s infrastructure. Three steady-state test scenarios were simulated to assess voltage stability, and power flow under: 1) baseline grid operation without PV generation, 2) distributed PV integration under normal load conditions, and 3) high-demand operation near generationload equilibrium. Results show that PV integration improves voltage regulation and reduces losses through localized energy injection and bidirectional power flow. However, under peak load conditions, the system exhibits significant undervoltage, revealing the need for advanced control strategies and infrastructure reinforcement. Overall, the DT proves to be an effective analytical and decision-support tool for optimising distributed energy systems. This work provides a replicable application-oriented framework for data-driven planning in emerging Renewable Energy Communities and supports Culatra Island’s transition toward full energy self-sufficiency. Unlike prior studies that report generalized benefits of PV integration, this work explicitly identifies voltage instability thresholds under high-demand conditions in a real REC configuration, providing actionable insight into when passive operation becomes insufficient.
- Impact of employing weather forecast data as input to the estimation of evapotranspiration by deep neural network modelsPublication . Migueis Vaz Martins, Pedro Jorge; Schütz, Gabriela; Guerrero, Carlos; Cardoso, PedroReference Evapotranspiration (𝐸𝑇!) is a key parameter for designing smart irrigation scheduling, since it is related by a coefficient to the water needs of a crop. The United Nations Food and Agriculture Organization, proposed a standard method for 𝐸𝑇! computation (FAO56PM), based on the parameterization of the Penman-Monteith equation, that is widely adopted in the literature. To compute 𝐸𝑇! using the FAO56-PM method, four main weather parameters are needed: temperature, humidity, wind, and solar radiation (SR). One way to make daily 𝐸𝑇! estimations for future days is to use freely available weather forecast services (WFSs), where many meteorological parameters are estimated up to the next 15 days. A problem with this method is that currently, SR is not provided as a free forecast parameter on most of those online services or, normally, such forecasts present a financial cost penalty. For this reason, several 𝐸𝑇! estimation models using machine and deep learning were developed and presented in the literature, that use as input features a reduced set of carefully selected weather parameters, that are compatible with common freely available WFSs. However, most studies on this topic have only evaluated model performance using data from weather stations (WSs), without considering the effect of using weather forecast data. In this study, the performance of authors’ previous models is evaluated when using weather forecast data from two online WFSs, in the following scenarios: (i) direct 𝐸𝑇! estimation by an Artificial Neural Network (ANN) model, and (ii) estimate SR by (another) ANN model, and then use that estimation for 𝐸𝑇! computation, using the FAO56-PM method. Employing data collected from two WFSs and a WS located in Vale do Lobo, Portugal, the latter approach achieved the best result, with a coefficient of determination (𝑅") ranging between 0.893 and 0.667, when considering forecasts up to 15 days.
- FlexiDialogue: Integrating dialogue trees for mental health with large language modelsPublication . Fernandes, João; Antunes, Ana; Campos, Joana; Dias, João; Santos, PedroThe increasing prevalence of mental health issues among university students is exacerbated by limited access to support due to shortages of mental health professionals and the stigma associated with seeking help. Virtual mental health assistants can extend the reach of existing resources, but traditional systems reliant on scripted dialogues are constrained by inflexibility and limited adaptability to diverse user inputs. This paper introduces FlexiDialogue, a system that transforms rigid dialogue trees into instruction sets for large language models, facilitating dynamic, contextually appropriate, and multilingual interactions while maintaining the structure and quality of expert-validated dialogue flows. The system was evaluated in three phases: (1) determining how effectively large language models could map open-ended user responses to predefined dialogue tree options, allowing for more natural interaction without compromising control; (2) assessing the models’ ability to paraphrase scripted dialogues to improve conversational fluidity while remaining grounded in the original tree; and (3) conducting an expert review to assess overall performance. Results demonstrated that FlexiDialogue enhanced the flexibility and coherence of interactions, with expert evaluations confirming its potential for mental health support.
- MentalRAG: developing an agentic framework for therapeutic support systemsPublication . Silva, Francisco; Santos, Pedro; Dias, JoãoThis paper introduces MentalRAG, a multi-agent system built upon an agentic framework designed to support mental health professionals through the automation of patient data collection and analysis. The system effectively gathers and processes high-sensitivity mental health data from users. It employs locally run opensource models for most tasks, while leveraging advanced state-of-the-art models for more complex analyses, ensuring the maintenance of data anonymity. The system’s models have showed improvements in delivering empathetic and contextually adaptive responses, particularly in sensitive contexts such as emotional distress and crisis management. Notably, an integrated agent for detecting levels of suicidal ideation allows the system to assess and respond sensitively to diverse levels of risk, promptly alerting mental health professionals as needed. This innovation represents a stride towards creating a more reliable, efficient, and ethically responsible mental health support tool, capable of addressing both patient and doctor needs effectively while minimizing associated risks.
- Wind turbines drive train fault detection: random forests vs CNNsPublication . Daniel, Helder; Baltazar, Sérgio; Li, Chuan; LUÍS VALENTE DE OLIVEIRA, JOSÉThe production of wind-powered energy is harvested by huge wind turbines installed in locations where winds are strong but difficult to access. Detecting minor severity faults allows for the scheduling of defective component replacement during planned maintenance dates, before the fault severity increases. This significantly reduces maintenance costs. A key component of wind turbines is the drivetrain, which transfers mechanical energy from the rotating blades to an electric energy generator. This gearbox system is quite exposed to faults, such as damaged gears and broken or worn teeth. This paper presents and discuss the identification and classification of gearbox faults using vibration and acoustic emission signals. It is shown that Random Forests (RFs) classifiers can be trained to achieve 100% accuracy rate, by performing previously classical feature extraction[8] on the raw signals, while Convolutional Neural Networks (CNNs) classifiers also achieve 100% accuracy rates, directly on raw signals and with a shorter duration than required by RF classifiers.
- Transdisciplinary ecohydrology for water management solutions and sustainabilityPublication . Elfithri, Rahmah; Zalewski, Maciej; Arduino, Giuseppe; Chicharo, LuisEcohydrology is a transdisciplinary and applied science, a sub-discipline of hydrology that seeks to understand and apply the natural ecological processes controlled by the hydrological cycle and vice versa1. This ‘dual regulation’2–4 knowledge is used to develop solutions restoring impacted aquatic ecosystems at the entire river system, where a starting point for the regulation of processes is the identification of a hierarchy of drivers at different ecosystems5. It is a solution-oriented science for reducing anthropogenic impacts, enhancing aquatic ecosystems and their catchment sustainability, improving water resources, bioproductivity and biodiversity, ecosystem services6 and resilience at the whole mesocycle, while the involvement of society through water culture and water education will be crucial for effective implementation. The continuous development of science-based solutions must be accompanied by dissemination and education about the concept of ecohydrology and its potential application and solutions.
- Synthetic data for robust identification of typical and atypical serotonergic neurons using convolutional neural networksPublication . Corradetti, Daniele; Bernardi, Alessandro; Corradetti, RenatoSerotonergic neurons in the raphe nuclei exhibit diverse electrophysiological properties and functional roles, yet conventional identification methods rely on restrictive criteria that likely overlook atypical serotonergic cells. The use of convolutional neural network (CNN) for comprehensive classification of both typical and atypical serotonergic neurons is an interesting one, but the key challenge is often given by the limited experimental data available for training. This study presents a procedure for synthetic data generation that combines smoothed spike waveforms with heterogeneous noise masks from real recordings. This approach expanded the training set while mitigating overfitting of background noise signatures. CNN models trained on the augmented dataset achieved high accuracy (96.2% true positive rate, 88.8% true negative rate) on non-homogeneous test data collected under different experimental conditions than the training, validation and testing data.
- Parasite diversity in plaice (Pleuronectes platessa): potential tool for stock identification in Icelandic waters?Publication . Pubert, Eve-Marine; Randhawa, Haseeb S.Understanding the stock structure of a commercial species is essential for sustainable 36 management. Failure to do so can lead to the depletion of regional sub-populations, erosion 37 of genetic diversity, and ecosystem services loss. Plaice, Pleuronectes platessa, is a 38 commercially exploited species inhabiting the continental shelf around Iceland. Despite a 39 tagging study providing support for strong spawning site and feeding ground fidelity, and 40 otolith microstructure analysis revealing local population structure, plaice is managed as a 41 single stock in Icelandic waters. Here, we describe and quantify the parasite fauna of plaice, 42 and assess the potential of parasites as biological tags for stock identification of plaice in 43 Icelandic waters. A total of 82 plaice were sampled from different geographical locations 44 (North and South) and seasons (summer and winter) in Iceland. Our sampling identified 11 45 parasites, five of which are new parasite records for plaice in Icelandic waters: the trematodes 46 Zoogonoides viviparus (adults) and Rhipidocotyle sp. (metacercariae), and the nematodes 47 Contracaecum osculatum (larvae), Dichelyne sp. (adults), and Hysterothylacium aduncum 48 (larvae and adults). Additionally, we recovered metacercariae of the trematode genus 49 Apatemon, which has not been recorded previously from plaice. Two parasites were 50 identified as potential biological tags for stock identification, namely the nematode A. simplex 51 and the trematode Z. viviparus. Our findings support a complex stock structure for plaice in 52 Icelandic waters and the need for an integrative strategy to stock identification to provide fine 53 spatial scale data required to inform fisheries managers.
- Onde está o “M” em uma tarefa integrada de STEM? ponto de vista de alunos do ensino básicoPublication . Quartieri, Marli Teresinha; Amado, Nélia Maria; Carreira, SusanaO objetivo desta pesquisa, de cunho qualitativo, foi conhecer o ponto de vista de alunos do Ensino Básico sobre a possibilidade de estabelecerem conexões matemáticas e de adquirirem conhecimentos matemáticos durante a resolução de uma tarefa STEM. Assim, foi proposta aos alunos de quatro turmas do 9.º ano (14-15 anos) uma tarefa STEM, com ênfase em S, T e M, que permitia o estabelecimento de conexões matemáticas. O tema foi aterosclerose, cujo desenvolvimento teve o intuito de elaborar um modelo matemático, traduzido por uma função quadrática que descrevesse a redução da quantidade de sangue com o aumento da espessura da placa de gordura, observada por meio de uma simulação. A aula em que foi desenvolvida a tarefa envolveu os seguintes momentos: i) apresentação da tarefa, simulação e resolução da situação, elaboração de um relatório; ii) identificação de conexões realizadas; iii) reflexão sobre a tarefa. Neste artigo, analisamos, por meio do método indutivo, os dados do segundo e terceiro momentos. Os resultados mostram que os alunos conseguiram estabelecer conexões intramatemáticas, extramatemáticas e as que definimos sem conexão com a matemática. Entretanto, houve dificuldades para justificá-las, em particular as primeiras. Em relação às aprendizagens, os alunos destacaram ter adquirido conhecimentos relativos à doença, deixando os matemáticos em segundo plano. Diante deste estudo, entendemos ser relevante pesquisas sobre como a matemática deve ser incluída em tarefas STEM, que os estudantes percebam a importância da matemática e que ela não fique em segundo plano em relação aos conhecimentos adquiridos.
- Identifying genetic markers for teak resistance to Ceratocystis wilt through associative mappingPublication . Vera dos Anjos, Isabela; Gilio, Thiago A. S.; Amorim, Ana Flávia S.; Chimello, Antonio M.; Jesus, Jeferson G. de; Palacios, Sthefany dos Santos M.; Cassaro, Sabrina; Takizawa, Fausto H.; Araújo, Kelly Lana; Neves, Leonarda Grillo; Araújo, Maria do Socorro B. deCeratocystis wilt, caused by the fungus Ceratocystis fimbriata, is one of the most important problems in teak (Tectona grandis) production, negatively affecting yield and wood quality. In this study, we aimed to use whole-genome sequencing to identify single-nucleotide polymorphisms (SNPs) associated with teak resistance to the fungus C. fimbriata in T. grandis. The resistance of 130 teak genotypes to the fungus was evaluated using the bark substitution method, and lesion area was assessed at 120 dpi. Through genotyping-by-sequencing analysis, 1.4 million high-quality SNPs were obtained and used for genome-wide association studies via FarmCPU model. The model demonstrated a good fit for the data and showed high levels of significance for the identified SNP variations. We identified three candidate SNP variations linked to the lesion area trait associated with teak resistance to Ceratocystis wilt. Specifically, one SNP variation is located on pseudochromosome 2, while two SNP variations are found on pseudochromosome 15. These findings can be applied in teak breeding programs aimed at enhancing resistance to the fungus C. fimbriata, either by using resistant clones directly or by incorporating these SNPs as markers for assisted selection in breeding programs.