Percorrer por autor "Carvalho, D."
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- Addressing critical fungal pathogens under a One Health perspective: key insights from the Portuguese Association of Medical MycologyPublication . Sabino, Raquel; Antunes, F.; Araujo, Ricardo; Bezerra, Ana Rita; Brandão, João; Carneiro, C.; Carvalho, Agostinho; Carvalho, D.; Conceição, I. C.; Medeiros, F. Cota; Cruz, C.; Duarte, E.; Holum, Stephanie; Matos, O.; Maltez, Fernando; Mendonça, Alexandre; Moura, Gabriela; Pereira, A.; Rodrigues, Célia Fortuna; Teixeira, P.; Valdoleiros, S. R.; Veríssimo, C.; Viegas, CarlaFungal infections have emerged as a significant public health concern, especially with the increasing incidence of severe mycoses caused by pathogens such as Aspergillus fumigatus, Candida auris, Candida albicans, and Cryptococcus neoformans. These fungi, listed as critical priorities by the World Health Organization, pose a heightened risk due to the rising prevalence of antifungal resistance and their severe impact on immunocompromised individuals. This article, coordinated by the Portuguese Association of Medical Mycology, emphasizes the importance of adopting a One Health approach to comprehensively address fungal threats. Drawing on interdisciplinary collaboration, the association aims to foster greater awareness, improve diagnostic capabilities, and stimulate research and public health policies in Portugal, but also at the global level. The paper outlines key strategies for surveillance, prevention, and innovation in fungal diagnostics and therapeutics. Moreover, it emphasizes the urgent need for national coordination and international cooperation in managing fungal infections, advocating for integrative approaches that link human, animal, and environmental health. By presenting a consolidated overview of current challenges and future priorities, this work seeks to enhance preparedness and response mechanisms in the face of escalating fungal threats.
- Wind power forecasting with machine learning: single and combined methodsPublication . Rosa, J.; Pestana, Rui; Leandro, Carlos; Brás-Geraldes, Carlos; Esteves, João; Carvalho, D.In Portugal, wind power represents one of the largest renewable sources of energy in the national energy mix. The investment in wind power started several decades ago and is still on the roadmap of political and industrial players. One example is that by 2030 it is estimated that wind power is going to represent up to 35% of renewable energy production in Portugal. With the growth of the installed wind capacity, the development of methods to forecast the amount of energy generated becomes increasingly necessary. Historically, Numerical Weather Prediction (NWP) models were used. However, forecasting accuracy depends on many variables such as on-site conditions, surrounding terrain relief, local meteorology, etc. Thus, it becomes a challenge to obtain improved results using such methods. This article aims to report the development of a machine learning pipeline with the objective of improving the forecasting capability of the NWP’s to obtain an error lower than 10%.